Relative fitness in a field setting
December 2022
#Background At Blue Oak Ranch Reserve, we established a 10m x 26m fenced field site to test whether evolution over the course of invasion away from roads has resulted in enhanced performance in undisturbed vegetation relative to roadside populations. The experiment was replicated in a randomized block design (20 plots in total). Each plot was 1.5m2 with 16 D. graveolens growing in a 4 x 4 grid centered on the plot. There was 33cm between each plant and 25cm between the edge plants and the border of the plot.
The experiment included multiple treatments; however, only the two most relevant to the focus of this paper are included here. We tested whether plant genotypes collected from the two habitats responded differently to the disturbance of biomass removal. We tilled in December 2020 to completely remove below and aboveground biomass, and then weeded to remove aboveground biomass throughout the growing season. In contrast, we left the control plots untouched, allowing the previous year’s thatch to persist and background vegetation to grow throughout the experiment.
In January 2021, we germinated seeds in Petri dishes at the UCSC Coastal Science Campus greenhouse incubation chambers before transplanting them into field-collected soil (collected in late December 2020 from Blue Oak Ranch Reserve). Seedlings grew in the greenhouse for about eight weeks until all plants had their first two true leaves emerge and lengthen. Ideally, we would have placed seeds directly into the field, but to maximize biosafety, we used seedling transplants that could be tracked with 100% certainty.
We measured the longest leaf for each plant and then transplanted them into the ground in late February 2021 at Blue Oak Ranch Reserve. During the first month of growth, we replaced any D. graveolens that died. We conducted weekly phenology surveys to assess D. graveolens plant health, and at the first sign of buds, we measured plant height and harvested the aboveground biomass by cutting at the root crown and drying in a 60ºC oven for 3 days before weighing.
#Data Analysis Statistical analyses were performed in R version 4.2.1 (R Core Team 2022) using linear mixed-effects models with the lme4 (Bates et al. 2015), lmerTest (Kuznetsova et al. 2017), and DHARMa packages (Hartig 2022), generalized linear mixed models with the glmmTMB package (Brooks et al. 2017), and mixed effects cox models with the coxme (Therneau 2022a) and survival (Therneau 2022b) packages.
##Models Included
###Cox proportional hazard models Here we will use ‘coxme’ which allows you to conduct mixed effects Cox proportional hazards models. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. We conducted a germination experiment using Dittrichia graveolens seeds on filter paper. “Surv” creates a survival object to combine the days column (NumDaysAlive) and the censor column (Censor) to be used as a response variable in a model formula.The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: “var1 * var2” will give you the interaction term and the individual variables: so “var1 + var2 + var1:var2”. To add random effects, type “+ (1|random effect)”.
Assumptions for cox models: https://www.theanalysisfactor.com/assumptions-cox-regression/
###Linear mixed-effects models Linear Mixed Effects models are used for regression analyses involving dependent data. For a tutorial: https://doi.org/10.1177/2515245920960351
fullmodel<-lmer(log(Biomass)~ #Response variable: biomass HabitatTreatment+ #Fixed effects and their interactions() (1|Site)+(1|Block), #Random effect with random intercept only data=mydata) #Dataframe
###Generalized linear mixed models under construction
#Libraries
#install.packages("coxme")
#install.packages("survival")
#install.packages("ggplot2")
#install.packages("ggfortify")
#install.packages("car")
#install.packages("multcomp")
#install.packages("lme4")
#install.packages("lmerTest")
#install.packages("DHARMa")
#install.packages("dplyr")
#install.packages("emmeans")
#install.packages('TMB', type = 'source')
#install.packages("glmmTMB")
#install.packages("MASS")
#install.packages("emmeans")
#install.packages("AICcmodavg")
library(coxme)
library(survival)
library(ggplot2)
library(ggfortify)
library(car)
library(multcomp)
library(lme4)
library(lmerTest)
library(DHARMa)
library(dplyr)
library(emmeans)
library(TMB)
library(glmmTMB)
library(MASS)
library(emmeans)
library(AICcmodavg)
library(tidyverse)#Load Data This dataframe has one row per plant (800 observations). Data are for survivorship curves (3 censor options), the number of days the plant stayed alive (NumDaysAlive) and aboveground biomass. Censors with a 1 denote reaching the event (CensorAll = died, CensorBiomass = survived to collect biomass, CensorReproduction = survived to reproduce) and a 0 denoting when a seed didn’t germinate by the last census date (Census = 11/15/21). CensorReproduction will be most useful in understanding the amount of biomass produced by an individual when buds appear.
mydata<-read.csv("/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/field_relative_fitness_2021.csv",stringsAsFactors=T)
str(mydata) #Check that each column has the right class (factor, integer, numeric, etc.)'data.frame': 800 obs. of 30 variables:
$ Block : Factor w/ 10 levels "A","B","C","D",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Plot : int 5 2 2 1 5 1 1 4 4 2 ...
$ Flag : Factor w/ 50 levels "A1","A2","A3",..: 5 7 12 16 25 26 31 39 44 47 ...
$ Pos : int 3 15 13 13 6 10 8 4 12 3 ...
$ Flag_Pos : Factor w/ 800 levels "A1_01","A1_02",..: 67 111 189 253 390 410 488 612 700 739 ...
$ Population : Factor w/ 16 levels "BAY-A","BAY-O",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Site : Factor w/ 8 levels "BAY","CHE","GUA",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Habitat : Factor w/ 2 levels "Roadside","Vegetated": 1 1 1 1 1 1 1 1 1 1 ...
$ Treatment : Factor w/ 5 levels "Biomass Removal",..: 1 1 1 1 1 1 1 1 1 1 ...
$ PlantDate : Factor w/ 8 levels "2/27/21","3/1/21",..: 5 1 1 1 1 1 8 1 1 1 ...
$ LeafMeas1 : num 30 20.3 27 14.9 16.7 ...
$ LeafMeas2 : int 46 51 NA 56 42 45 36 58 52 45 ...
$ Growth : num 16 30.7 -27 41 25.3 ...
$ MortDate : Factor w/ 29 levels "","10/1/21","11/14/21",..: 1 25 7 1 1 1 1 1 1 1 ...
$ MortHarvDate : Factor w/ 33 levels "10/1/21","10/28/21",..: 21 27 9 29 20 29 29 29 30 30 ...
$ CensorAll : int 1 1 1 1 1 1 1 1 1 1 ...
$ DaysMort : int 105 181 62 195 139 195 185 195 198 198 ...
$ Census : Factor w/ 1 level "11/15/21": 1 1 1 1 1 1 1 1 1 1 ...
$ DaysCensus : int 241 261 261 261 261 261 251 261 261 261 ...
$ NumDaysAlive : int 105 181 62 195 139 195 185 195 198 198 ...
$ HarvDate : Factor w/ 22 levels "","10/1/21","10/28/21",..: 8 15 1 17 7 17 17 17 18 18 ...
$ CensorBiomass : int 1 1 0 1 1 1 1 1 1 1 ...
$ BudDate : Factor w/ 20 levels "","10/1/21","10/28/21",..: 9 1 1 17 11 17 17 17 17 17 ...
$ CensorReproduction: int 1 0 0 1 1 1 1 1 1 1 ...
$ CensorTest : int 0 1 1 0 0 0 0 0 0 0 ...
$ PropBud : num 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 ...
$ PropBudSite : num 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ...
$ Height : num 12.4 33.8 NA 56.6 21.5 55.2 45.2 37.5 50.8 48.5 ...
$ Biomass : num 0.762 7.048 NA 7.803 2.572 ...
$ Biomass.date : Factor w/ 18 levels "","0.561","10/13/21",..: 17 16 1 8 15 11 11 11 10 10 ...mydata$Site<-as.character(mydata$Site)
mydata$Treatment<-factor(mydata$Treatment,
levels=c("Grassland","Biomass Removal","Hemizonia","Raking","Raking & Clipping")) #Changing the contrast order so that everything is compared to Grassland (control)#Early Growth This code uses Growth data with Habitat (roadside and vegetated) and Treatment in a glmm model. Anova and Tukey tests are used on the successful Model4 with the creation of a box plot as a finished product.
Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)
Also Note: Growth data is a measure of growth of longest leaf. Each plant was measured upon transplanting into the field in March and then again in June 2021. This plant has a juvenile stage of a basal rosette, and then it bolts and produced smaller cauline leaves. The goal was to capture early growth data for this plant before bolting occurs so that the measurements capture the basal rosette stage, however in some cases the plants bolted earlier than expected and the resulting measurement was smaller than previous. In these cases we determined that the data should be removed from the dataset as the negative number (or changing it to a zero) does not reflect the biological importance of the measurement.
Here we need to filter the data to remove Growth>0 and to convert plant measurement dates to date format
growth_mydata<-mydata%>%filter(Growth>0) #Here I am only looking at the Growth data that is greater than 0 (see Also Note above)
growth_mydata$PlantDate<-as.Date(growth_mydata$PlantDate,"%m/%d/%y")
growth_mydata$Num.Days.Growth<-as.Date("2021-05-22")-growth_mydata$PlantDate
growth_mydata$Num.Days.Growth<-as.numeric(growth_mydata$Num.Days.Growth)
growth_mydata$Growth.Rate<-growth_mydata$Growth/growth_mydata$Num.Days.Growth #First calculate number of days of growth to get the Rate (Growth/Num.Days.Growing). Then fit data to Beta distribution##Histograms Original data
hist(growth_mydata$Growth.Rate,
col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data
shapiro.test(growth_mydata$Growth.Rate)
Shapiro-Wilk normality test
data: growth_mydata$Growth.Rate
W = 0.90445, p-value < 2.2e-16Log transform data (https://www.statology.org/transform-data-in-r/)
log_growth_mydata<-log10(growth_mydata$Growth.Rate)
hist(log_growth_mydata,
col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution
shapiro.test(log_growth_mydata) #Data does not improve with log transformation
Shapiro-Wilk normality test
data: log_growth_mydata
W = 0.92178, p-value = 1.503e-15Next I tried a square root transformation, which improved the distribution, but my first model failed the singular fit (see Model 1).
sqrt_growth_mydata<-sqrt(growth_mydata$Growth.Rate)
hist(sqrt_growth_mydata,
col='steelblue',main='Log Transformed') #Square root transformed data looks better than the original distribution
shapiro.test(sqrt_growth_mydata)
Shapiro-Wilk normality test
data: sqrt_growth_mydata
W = 0.98581, p-value = 7.695e-05##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects
growth_fullmodel1<-lmer(sqrt(Growth.Rate)~Habitat*Treatment+(1|Site)+(1|Block),data=growth_mydata)boundary (singular) fit: see help('isSingular')isSingular(growth_fullmodel1,tol=1e-4) #=True[1] TRUEsummary(growth_fullmodel1) #Variance explained by Site = 0.000Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sqrt(Growth.Rate) ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Data: growth_mydata
REML criterion at convergence: -583.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.61595 -0.69539 0.05978 0.73053 2.88562
Random effects:
Groups Name Variance Std.Dev.
Block (Intercept) 0.001598 0.03998
Site (Intercept) 0.000000 0.00000
Residual 0.016171 0.12716
Number of obs: 506, groups: Block, 10; Site, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.271164 0.024328 78.070400 11.146 < 2e-16 ***
HabitatVegetated 0.023733 0.028530 487.902365 0.832 0.40590
TreatmentBiomass Removal 0.299248 0.025549 488.988743 11.713 < 2e-16 ***
TreatmentHemizonia 0.009046 0.029694 488.862262 0.305 0.76078
TreatmentRaking 0.037015 0.028063 487.757926 1.319 0.18778
TreatmentRaking & Clipping 0.073071 0.027082 488.008833 2.698 0.00722 **
HabitatVegetated:TreatmentBiomass Removal -0.005098 0.035439 487.461999 -0.144 0.88568
HabitatVegetated:TreatmentHemizonia 0.014342 0.041419 487.869509 0.346 0.72928
HabitatVegetated:TreatmentRaking -0.022588 0.038582 488.126281 -0.585 0.55852
HabitatVegetated:TreatmentRaking & Clipping -0.006018 0.037436 488.124609 -0.161 0.87236
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.614
TrtmntBmssR -0.694 0.584
TretmntHmzn -0.596 0.501 0.567
TretmntRkng -0.626 0.532 0.597 0.512
TrtmntRkn&C -0.650 0.551 0.619 0.533 0.560
HbttVgt:TBR 0.493 -0.804 -0.714 -0.402 -0.428 -0.442
HbttVgtt:TH 0.418 -0.687 -0.398 -0.708 -0.365 -0.378 0.553
HbttVgtt:TR 0.454 -0.741 -0.432 -0.369 -0.726 -0.406 0.596 0.508
HbttVg:TR&C 0.465 -0.761 -0.442 -0.381 -0.403 -0.721 0.612 0.526 0.564
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')anova(growth_fullmodel1)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Habitat 0.0464 0.04642 1 488.79 2.8709 0.09083 .
Treatment 7.4582 1.86456 4 489.79 115.3056 < 2e-16 ***
Habitat:Treatment 0.0146 0.00366 4 487.95 0.2260 0.92382
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Site as a random effect does not explain any of the variance in the model, therefore let’s try Site as a fixed effect to see if it adds to the model.
##Model 2 - lmer: Modeling Site as a fixed effect and Block as a random effect
growth_fullmodel2<-lmer(log(Growth.Rate)~
Habitat*Treatment+
Site+
(1|Block),
data=growth_mydata)
summary(growth_fullmodel2) #As a fixed effect, one of the Sites (OAP) is significant.Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Growth.Rate) ~ Habitat * Treatment + Site + (1 | Block)
Data: growth_mydata
REML criterion at convergence: 1374.5
Scaled residuals:
Min 1Q Median 3Q Max
-6.2000 -0.4782 0.1779 0.6492 1.8316
Random effects:
Groups Name Variance Std.Dev.
Block (Intercept) 0.06789 0.2606
Residual 0.82580 0.9087
Number of obs: 506, groups: Block, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.984938 0.204110 165.530302 -14.624 < 2e-16 ***
HabitatVegetated 0.220653 0.204078 480.955621 1.081 0.28014
TreatmentBiomass Removal 1.729151 0.183091 482.054827 9.444 < 2e-16 ***
TreatmentHemizonia 0.196272 0.212666 481.942503 0.923 0.35651
TreatmentRaking 0.346745 0.201477 480.773125 1.721 0.08589 .
TreatmentRaking & Clipping 0.547314 0.194472 480.894171 2.814 0.00509 **
SiteCHE 0.039617 0.163190 481.278240 0.243 0.80829
SiteGUA -0.006678 0.163292 481.229118 -0.041 0.96740
SiteLEX -0.063730 0.164176 481.225590 -0.388 0.69805
SiteOAP 0.329379 0.161428 481.228603 2.040 0.04186 *
SitePAR 0.105935 0.165141 480.587844 0.641 0.52152
SitePEN 0.115262 0.167219 481.510246 0.689 0.49098
SiteSSJ 0.112886 0.166554 480.788469 0.678 0.49824
HabitatVegetated:TreatmentBiomass Removal -0.152103 0.253524 480.438799 -0.600 0.54882
HabitatVegetated:TreatmentHemizonia 0.048304 0.296429 480.886893 0.163 0.87062
HabitatVegetated:TreatmentRaking -0.214508 0.276049 481.225843 -0.777 0.43750
HabitatVegetated:TreatmentRaking & Clipping -0.193752 0.268159 481.243075 -0.723 0.47032
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation matrix not shown by default, as p = 17 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itanova(growth_fullmodel2) #Site as a fixed effect accounts for 3% of the varianceType III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Habitat 1.636 1.636 1 482.01 1.9816 0.1599
Treatment 211.245 52.811 4 483.04 63.9513 <2e-16 ***
Site 6.623 0.946 7 481.06 1.1457 0.3331
Habitat:Treatment 1.190 0.297 4 481.03 0.3601 0.8370
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Now we’ll look at the QQ plots and the residuals using DHARMa
qqnorm(resid(growth_fullmodel2)) #qqplot
qqline(resid(growth_fullmodel2)) #add the line
testDispersion(growth_fullmodel2) #red line should be in the middle of the distribution
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.96957, p-value = 0.664
alternative hypothesis: two.sided
myDHARMagraph2<-simulateResiduals(growth_fullmodel2) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph2) #plotting graph. At this point, you don't want any text or lines to be red. The QQ plots are not looking good for this model. Let's try fitting to a negative binomial distribution.
##Model 3 - glmer.nb: Negative Binomial
So this model isn’t working well either. Let’s try building a model and fitting it to a Beta distribution, first without a link function. Check it with DHARMa, and if it doesn’t look good, then fit it to a Beta distribution with a logit link function.
##Model 4 - glmm: Beta Distribution Now I’m using glmm because I’m fitting to other distributions (beta)
growth_fullmodel4<-glmmTMB(Growth.Rate~
Habitat*Treatment+
(1|Site)+
(1|Block),
family=beta_family(),
data=growth_mydata)
summary(growth_fullmodel4) Family: beta ( logit )
Formula: Growth.Rate ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Data: growth_mydata
AIC BIC logLik deviance df.resid
-989.3 -934.4 507.7 -1015.3 493
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Site (Intercept) 0.0002645 0.01626
Block (Intercept) 0.0429336 0.20720
Number of obs: 506, groups: Site, 8; Block, 10
Dispersion parameter for beta family (): 11.4
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.27681 0.14929 -15.251 <2e-16 ***
HabitatVegetated 0.12961 0.17864 0.726 0.468
TreatmentBiomass Removal 1.60566 0.15070 10.655 <2e-16 ***
TreatmentHemizonia 0.07352 0.18737 0.392 0.695
TreatmentRaking 0.20030 0.17566 1.140 0.254
TreatmentRaking & Clipping 0.41358 0.16659 2.483 0.013 *
HabitatVegetated:TreatmentBiomass Removal -0.03513 0.20401 -0.172 0.863
HabitatVegetated:TreatmentHemizonia 0.10730 0.25788 0.416 0.677
HabitatVegetated:TreatmentRaking -0.09786 0.23867 -0.410 0.682
HabitatVegetated:TreatmentRaking & Clipping -0.06623 0.22709 -0.292 0.771
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1qqnorm(resid(growth_fullmodel4)) #qqplot
qqline(resid(growth_fullmodel4)) #add the line
testDispersion(growth_fullmodel4) #red line should be in the middle of the distribution
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.97597, p-value = 0.84
alternative hypothesis: two.sided
myDHARMagraph4<-simulateResiduals(growth_fullmodel4) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph4) #plotting graph. Looks good, but check the outliers to make sure they are real.
We should check the outliers in the the DHARMa plot to make sure they make sense.
growth_outlier_boxplot1<-ggplot(growth_mydata)+
geom_boxplot(aes(x=Habitat,
y=Growth.Rate),
size=0.5)+
theme_classic()+
ylim(0,1)+
scale_fill_manual(values=c("forestgreen","gray85"))+
labs(y="Early Growth Rate",
fill="Habitat",
x="Habitat")
growth_outlier_boxplot1
max(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Away"])Warning: no non-missing arguments to max; returning -Inf[1] -Infmax(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Roadside"]) #Yes, these outliers make sense, so I don't need to worry about the red stars in the DHARMa plot.[1] 0.75###Best Model
growth_fullmodel4<-glmmTMB(Growth.Rate~
Habitat*Treatment+
(1|Site)+
(1|Block),
family=beta_family(),
data=growth_mydata)
summary(growth_fullmodel4) Family: beta ( logit )
Formula: Growth.Rate ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Data: growth_mydata
AIC BIC logLik deviance df.resid
-989.3 -934.4 507.7 -1015.3 493
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Site (Intercept) 0.0002645 0.01626
Block (Intercept) 0.0429336 0.20720
Number of obs: 506, groups: Site, 8; Block, 10
Dispersion parameter for beta family (): 11.4
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.27681 0.14929 -15.251 <2e-16 ***
HabitatVegetated 0.12961 0.17864 0.726 0.468
TreatmentBiomass Removal 1.60566 0.15070 10.655 <2e-16 ***
TreatmentHemizonia 0.07352 0.18737 0.392 0.695
TreatmentRaking 0.20030 0.17566 1.140 0.254
TreatmentRaking & Clipping 0.41358 0.16659 2.483 0.013 *
HabitatVegetated:TreatmentBiomass Removal -0.03513 0.20401 -0.172 0.863
HabitatVegetated:TreatmentHemizonia 0.10730 0.25788 0.416 0.677
HabitatVegetated:TreatmentRaking -0.09786 0.23867 -0.410 0.682
HabitatVegetated:TreatmentRaking & Clipping -0.06623 0.22709 -0.292 0.771
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1###Post-Hoc Test Remove non-significant interaction terms before running the Tukey.
#?emmeans,emmeans(model,pairwise~treatment)
growth_fullmodel4.1<-glmmTMB(Growth.Rate~
Treatment+
(1|Site)+
(1|Block),
family=beta_family(),
data=growth_mydata)
summary(growth_fullmodel4.1) Family: beta ( logit )
Formula: Growth.Rate ~ Treatment + (1 | Site) + (1 | Block)
Data: growth_mydata
AIC BIC logLik deviance df.resid
-996.0 -962.2 506.0 -1012.0 498
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Site (Intercept) 0.0006051 0.0246
Block (Intercept) 0.0416553 0.2041
Number of obs: 506, groups: Site, 8; Block, 10
Dispersion parameter for beta family (): 11.3
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.2066 0.1144 -19.284 < 2e-16 ***
TreatmentBiomass Removal 1.5835 0.1053 15.043 < 2e-16 ***
TreatmentHemizonia 0.1210 0.1295 0.934 0.35019
TreatmentRaking 0.1507 0.1203 1.253 0.21035
TreatmentRaking & Clipping 0.3781 0.1150 3.287 0.00101 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1emmeans(growth_fullmodel4.1,
pairwise~Treatment)$emmeans
Treatment emmean SE df lower.CL upper.CL
Grassland -2.207 0.1144 498 -2.431 -1.982
Biomass Removal -0.623 0.0818 498 -0.784 -0.462
Hemizonia -2.086 0.1158 498 -2.313 -1.858
Raking -2.056 0.1050 498 -2.262 -1.850
Raking & Clipping -1.828 0.0969 498 -2.019 -1.638
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Grassland - Biomass Removal -1.5835 0.1053 498 -15.043 <.0001
Grassland - Hemizonia -0.1210 0.1295 498 -0.934 0.8835
Grassland - Raking -0.1507 0.1203 498 -1.253 0.7203
Grassland - Raking & Clipping -0.3781 0.1150 498 -3.287 0.0095
Biomass Removal - Hemizonia 1.4625 0.1071 498 13.658 <.0001
Biomass Removal - Raking 1.4327 0.0950 498 15.081 <.0001
Biomass Removal - Raking & Clipping 1.2054 0.0864 498 13.956 <.0001
Hemizonia - Raking -0.0298 0.1225 498 -0.243 0.9992
Hemizonia - Raking & Clipping -0.2571 0.1170 498 -2.198 0.1822
Raking - Raking & Clipping -0.2274 0.1062 498 -2.141 0.2043
Results are given on the log odds ratio (not the response) scale.
P value adjustment: tukey method for comparing a family of 5 estimates ###Chapter 1 three panel figure
###ggplot ####Interaction plot #####Biomass x Treatment: Growth
pd<-position_dodge(0)
growth_mydata1<-growth_mydata%>%
replace_na(list(Biomass=0))%>%
filter(CensorReproduction==1)%>%
filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
group_by(Treatment,Habitat)%>%
summarise(Mean=mean(Biomass),
SD=sd(Biomass),
N=length(Biomass))%>%
mutate(SE=SD/sqrt(N))`summarise()` has grouped output by 'Treatment'. You can override using the `.groups` argument.field_int_bio_grow_gg<-ggplot(growth_mydata1,
aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
y=Mean,
shape=Habitat,
group=Habitat,
color=Habitat))+
ylab("Biomass (g)")+
coord_cartesian(ylim = c(0,10))+
xlab("")+
#ggtitle("Biomass of Dittrichia graveolens that budded")+
theme_bw(base_size=16)+
theme(panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.line=element_line(colour="black"),
axis.title.y=ggtext::element_markdown(),
legend.position = c(0.8, 0.8))+
geom_line(size=1,
position=pd)+
geom_point(size=5,
position=pd)+
scale_color_manual(values=c("gray85","forestgreen"))+
geom_errorbar(width=0.15,
position=pd,
aes(ymin=(Mean-SE),
ymax=(Mean+SE)))+
labs(y="*Dittrichia graveolens* \n Biomass (g)",
color="Source Habitat",
shape="Source Habitat")
field_int_bio_grow_gg
#ggsave(plot=field_int_bio_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_bio_grow_gg.png",width=25,height=15,units="cm",dpi=800)#####Growth Rate x Treatment: Growth
pd<-position_dodge(0)
growth_mydata2<-growth_mydata%>%
replace_na(list(Growth.Rate=0))%>%
filter(CensorReproduction==1)%>%
filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
group_by(Treatment,Habitat)%>%
summarise(Mean=mean(Growth.Rate),
SD=sd(Growth.Rate),
N=length(Growth.Rate))%>%
mutate(SE=SD/sqrt(N))`summarise()` has grouped output by 'Treatment'. You can override using the `.groups` argument.field_int_rate_grow_gg<-ggplot(growth_mydata2,
aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
y=Mean,
shape=Habitat,
group=Habitat,
color=Habitat))+
coord_cartesian(ylim = c(0,0.5))+
xlab("")+
theme_bw(base_size=16)+
theme(panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.line=element_line(colour="black"),
axis.title.y=ggtext::element_markdown(),
legend.position = c(0.8, 0.8))+
# ggtitle("Growth of Dittrichia graveolens that budded")+
ylab("*Dittrichia graveolens* \n (Change in Leaf Length/Days)")+
geom_line(size=1,
position=pd)+
geom_point(size=5,
position=pd)+
scale_color_manual(values=c("gray85","forestgreen"))+
geom_errorbar(width=0.15,
position=pd,
aes(ymin=(Mean-SE),
ymax=(Mean+SE)))+
labs(y="Change in *Dittrichia graveolens* Leaf Length/Day",
color="Source Habitat",
shape="Source Habitat")
field_int_rate_grow_gg
#ggsave(plot=field_int_rate_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_rate_grow_gg.png",width=25,height=15,units="cm",dpi=800)####Boxplot
#Boxplot with Early Growth Rate by Habitat
growth_boxplot1<-ggplot(growth_mydata)+
geom_boxplot(aes(x=Habitat,
y=Growth.Rate),
size=0.5)+
theme_classic()+
ylim(0,1)+
scale_fill_manual(values=c("forestgreen","gray58"))+
labs(y="Early Growth Rate",
fill="Habitat",
x="Habitat")
growth_boxplot1
#ggsave(plot=growth_boxplot1,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplot1.png",width=20,height=20,units="cm",dpi=800)
#Boxplot with Early Growth Rate by All Treatments
growth_boxplot2<-ggplot(growth_mydata)+
geom_boxplot(aes(x=Treatment,
y=Growth.Rate),
size=0.5)+
theme_classic()+
ylim(0,1)+
scale_fill_manual(values=c("forestgreen","gray85"))+
labs(y="Early Growth Rate",
fill="Habitat",
x="Treatment")
growth_boxplot2
#ggsave(plot=growth_boxplot2,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplot2.png",width=20,height=20,units="cm",dpi=800)
#Boxplot with Early Growth Rate by 2 treatments (Control and Biomass Removal)
sub_growth_mydata<-subset(growth_mydata,
Treatment%in%c('Control','Biomass Removal'))
growth_boxplot3<-ggplot(sub_growth_mydata)+
geom_boxplot(aes(x=Treatment,
y=Growth.Rate),
size=0.5)+
theme_classic()+
ylim(0,1)+
scale_fill_manual(values=c("forestgreen","gray85"))+
labs(y="Early Growth Rate",
fill="Habitat",
x="Treatment")
growth_boxplot3
#ggsave(plot=growth_boxplot3,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplo3.png",width=20,height=20,units="cm",dpi=800)#Reproductive Biomass This code uses Biomass data with Habitat (roadside and vegetated) and Treatment in a lmer model. ANOVA and Tukey are used on the successful Model 3 with the creation of a box plot as a finished product.
Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)
Also Note: Biomass data is a measure of aboveground biomass of all individuals harvested from the site. In some cases this was before they budded, but we harvested the wilted plant in case that information was needed in the future. Here we only want to look at biomass (well, log(Biomass)) for reproductive individuals.
Here we need to subset the data to only look at Biomass when CensorReproduction = 1, so that all Biomass data is for reproductive individuals only.
reproduction_mydata<-subset(mydata,CensorReproduction%in%c('1')) #Here I am only looking at the Biomass data where the CensorReproduction = 1##Histograms
hist(reproduction_mydata$Biomass,col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data
shapiro.test(reproduction_mydata$Biomass)
Shapiro-Wilk normality test
data: reproduction_mydata$Biomass
W = 0.62548, p-value < 2.2e-16Log transform data (https://www.statology.org/transform-data-in-r/)
log_reproduction_mydata<-log10(reproduction_mydata$Biomass)
hist(log_reproduction_mydata,col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution
shapiro.test(log_reproduction_mydata)
Shapiro-Wilk normality test
data: log_reproduction_mydata
W = 0.9648, p-value = 9.101e-06#Log transformed data has a better distribution than the original data so we will use the log transformed data with our models##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects
fullmodel1<-lmer(log(Biomass)~
Habitat*Treatment+
(1|Site)+
(1|Block),
data=reproduction_mydata)
isSingular(fullmodel1,tol=1e-4)[1] FALSEsummary(fullmodel1) #Variance explained by Site = 0.000Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Data: reproduction_mydata
REML criterion at convergence: 675.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2251 -0.6836 0.0555 0.6095 2.9640
Random effects:
Groups Name Variance Std.Dev.
Block (Intercept) 0.1836330 0.42852
Site (Intercept) 0.0003818 0.01954
Residual 0.8322599 0.91228
Number of obs: 247, groups: Block, 10; Site, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -1.47119 0.25189 65.18355 -5.841 1.8e-07 ***
HabitatVegetated 0.52617 0.31275 229.17635 1.682 0.0939 .
TreatmentBiomass Removal 3.16732 0.24296 227.53081 13.036 < 2e-16 ***
TreatmentHemizonia -0.05905 0.31382 229.47907 -0.188 0.8509
TreatmentRaking 0.25358 0.41060 211.72838 0.618 0.5375
TreatmentRaking & Clipping 0.54416 0.31428 229.25370 1.731 0.0847 .
HabitatVegetated:TreatmentBiomass Removal -0.59897 0.35315 229.14685 -1.696 0.0912 .
HabitatVegetated:TreatmentHemizonia -0.30666 0.44327 228.66064 -0.692 0.4898
HabitatVegetated:TreatmentRaking -0.20197 0.55618 220.56602 -0.363 0.7168
HabitatVegetated:TreatmentRaking & Clipping -0.79028 0.43205 229.37117 -1.829 0.0687 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.564
TrtmntBmssR -0.738 0.582
TretmntHmzn -0.558 0.439 0.582
TretmntRkng -0.445 0.351 0.463 0.342
TrtmntRkn&C -0.570 0.455 0.591 0.450 0.361
HbttVgt:TBR 0.500 -0.883 -0.675 -0.390 -0.309 -0.403
HbttVgtt:TH 0.392 -0.702 -0.404 -0.690 -0.246 -0.316 0.619
HbttVgtt:TR 0.319 -0.556 -0.331 -0.252 -0.723 -0.258 0.490 0.391
HbttVg:TR&C 0.413 -0.727 -0.425 -0.316 -0.262 -0.725 0.641 0.507 0.405anova(fullmodel1)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Habitat 0.84 0.843 1 229.56 1.0133 0.3152
Treatment 483.42 120.855 4 228.83 145.2134 <2e-16 ***
Habitat:Treatment 3.83 0.957 4 226.65 1.1497 0.3340
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#Site as a random effect does not explain any of the variance in the model, therefore let's try Site as a fixed effect to demonstrate that it doesn't add to the model.
predict(fullmodel1) 1 4 5 6 7 8 9 10 11 13
0.9783831 2.1411337 1.3028518 1.8487338 1.6247945 1.9815684 1.5191378 1.4884911 0.9832969 1.7787921
14 15 16 18 19 21 22 23 24 25
2.1460476 1.3077657 1.8536476 1.9864822 1.5240516 0.9837652 2.2719098 1.7792604 2.1465159 1.3082340
26 27 28 29 30 31 32 33 35 37
1.8541159 1.6301767 1.9869505 1.5245199 1.4938733 0.9834082 2.2715527 1.7789034 1.3078770 1.6298196
38 40 41 42 44 46 47 48 49 51
1.9865935 1.4935163 0.9827552 2.2708997 2.1455059 1.8531059 1.6291666 1.9859405 1.5235099 0.9783656
52 53 55 56 57 58 59 60 62 63
2.2665102 1.7738608 1.3028344 1.8487163 1.6247771 1.9815509 1.5191203 1.4884737 2.2710672 1.7784179
64 66 67 68 69 71 72 73 76 77
2.1456733 1.8532734 1.6293341 1.9861080 1.5236774 0.9827714 2.2709159 1.7782665 1.8531221 1.6291828
78 81 83 85 86 87 88 89 90 93
1.9859567 0.9055791 1.7010743 1.2300479 1.7759298 1.5519905 1.9087644 1.4463338 1.4156872 1.7059881
94 95 96 97 98 100 102 103 105 106
2.0732436 1.2349617 1.7808436 1.5569043 1.9136782 1.4206010 2.1991058 1.7064564 1.2354300 1.7813120
107 109 111 112 113 114 116 118 119 120
1.5573727 1.4517160 0.9106042 2.1987488 1.7060994 2.0733549 1.7809549 1.9137895 1.4513589 1.4207123
122 123 124 127 128 129 130 131 133 134
2.1980958 1.7054464 2.0727019 1.5563627 1.9131366 1.4507059 1.4200593 0.9055616 1.7010568 2.0683123
135 136 137 138 139 140 141 142 143 144
1.2300304 1.7759124 1.5519731 1.9087470 1.4463164 1.4156697 0.9101187 2.1982633 1.7056139 2.0728694
147 148 149 151 153 154 155 156 157 158
1.5565301 1.9133040 1.4508734 0.9099674 1.7054626 2.0727181 1.2344362 1.7803181 1.5563788 1.9131527
159 160 161 163 164 170 172 174 184 185
1.4507221 1.4200755 -2.1889361 -1.3934409 -1.0261854 -1.6788280 -0.8958777 -1.0212716 -1.0208033 -1.8590852
189 195 196 201 210 211 215 224 226 228
-1.6427992 -1.8594422 -1.3135603 -2.1845640 -1.6744559 -2.1889536 -1.8644848 -1.0216458 -1.3140458 -1.1812112
236 254 259 260 270 272 280 281 284 285
-1.3141971 -0.4951009 -1.1170969 -1.1477435 -1.1472751 -0.3695957 -1.1476322 -1.6583933 -0.4956426 -1.3339245
286 287 295 296 300 303 310 321 324 325
-0.7880426 -1.0119818 -1.3383141 -0.7924321 -1.1526748 -0.8627306 -1.1481177 -2.2479869 -1.0852362 -1.9235181
326 327 334 351 353 354 361 367 371 374
-1.3776362 -1.6015755 -1.0803224 -2.2429618 -1.4474666 -1.0802111 -2.2436148 -1.5972033 -2.2480044 -1.0852537
387 391 398 401 402 407 413 414 421 424
-1.5970359 -2.2435986 -1.2404133 -2.0284798 -0.7403353 -1.3820684 -1.2280708 -0.8608154 -2.0230977 -0.8603470
437 441 446 461 463 471 472 475 476 477
-1.3770433 -2.0241077 -1.1537570 -2.0239402 -1.2284451 -2.0240915 -0.7359470 -1.6996228 -1.1537408 -1.3776801
479 493 495 498 499 500 527 548 569 570
-1.4833368 -1.1349432 -1.6059696 -0.9272531 -1.3896837 -1.4203303 -1.2845687 -0.9276273 -1.0703998 -1.1010464
577 580 588 595 600 608 612 645 653 659
-0.9598293 -1.0961326 -0.6025870 -1.2816606 -1.0960213 -0.6035971 -0.3230274 -1.3203070 -0.8443668 -1.0991073
660 661 665 667 675 677 681 682 691 693
-1.1297539 -1.6393937 -1.3149249 -0.9929822 -1.3152819 -0.9933393 -1.6404037 -0.3522591 -1.6447933 -0.8492981
695 708 710 721 727 731 734 735 738 740
-1.3203245 -0.6370509 -1.1301281 -1.9088882 -1.2624768 -1.9039744 -0.7412237 -1.5795056 -0.9007890 -1.3938663
751 752 757 763 766 767 771 776 780 785
-1.9038631 -0.6157185 -1.2574516 -1.1090209 -1.0341654 -1.2581046 -1.9089056 -1.0385549 -1.3987976 -1.5798798
789 791 794 795 796 798 800
-1.3635939 -1.9044999 -0.7417492 -1.5800311 -1.0341492 -0.9013146 -1.3943918 hist(predict(fullmodel1,type="response"))
##Model 2 - lmer: Modeling Site as a fixed effect
fullmodel2<-lmer(log(Biomass)~
Habitat*Treatment+
Site+
(1|Block),
data=reproduction_mydata)
summary(fullmodel2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + Site + (1 | Block)
Data: reproduction_mydata
REML criterion at convergence: 678.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2839 -0.6210 0.0399 0.5639 2.7969
Random effects:
Groups Name Variance Std.Dev.
Block (Intercept) 0.1650 0.4062
Residual 0.8361 0.9144
Number of obs: 247, groups: Block, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -1.720266 0.285577 105.107873 -6.024 2.54e-08 ***
HabitatVegetated 0.488869 0.318875 223.009734 1.533 0.1267
TreatmentBiomass Removal 3.161011 0.244374 223.621849 12.935 < 2e-16 ***
TreatmentHemizonia -0.004785 0.317523 223.398880 -0.015 0.9880
TreatmentRaking 0.156859 0.426831 224.250434 0.367 0.7136
TreatmentRaking & Clipping 0.509433 0.317070 223.835290 1.607 0.1095
SiteCHE 0.355322 0.232174 223.497740 1.530 0.1273
SiteGUA 0.410402 0.245081 221.592351 1.675 0.0954 .
SiteLEX 0.356617 0.233876 222.425736 1.525 0.1287
SiteOAP 0.316128 0.234233 222.925291 1.350 0.1785
SitePAR -0.001442 0.231840 221.753218 -0.006 0.9950
SitePEN 0.347827 0.244024 223.399330 1.425 0.1554
SiteSSJ 0.315678 0.237514 222.147337 1.329 0.1852
HabitatVegetated:TreatmentBiomass Removal -0.556028 0.358662 222.367673 -1.550 0.1225
HabitatVegetated:TreatmentHemizonia -0.363654 0.452306 222.295756 -0.804 0.4223
HabitatVegetated:TreatmentRaking -0.059139 0.573956 221.764455 -0.103 0.9180
HabitatVegetated:TreatmentRaking & Clipping -0.744475 0.440856 223.170989 -1.689 0.0927 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation matrix not shown by default, as p = 17 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itanova(fullmodel2)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Habitat 0.80 0.799 1 222.81 0.9554 0.3294
Treatment 478.33 119.583 4 224.94 143.0199 <2e-16 ***
Site 5.74 0.820 7 222.78 0.9811 0.4458
Habitat:Treatment 3.43 0.857 4 222.42 1.0244 0.3955
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#Site as a fixed effect is not significant, therefore it should not be used as a fixed effect in addition to it not being used as a random effect.##Model 3 - lmer: Site is removed from this model because it explains very little of the variance
fullmodel3<-lmer(log(Biomass)~
Habitat*Treatment+
(1|Block),
data=reproduction_mydata)
summary(fullmodel3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + (1 | Block)
Data: reproduction_mydata
REML criterion at convergence: 675.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2235 -0.6825 0.0572 0.6111 2.9658
Random effects:
Groups Name Variance Std.Dev.
Block (Intercept) 0.1839 0.4288
Residual 0.8326 0.9124
Number of obs: 247, groups: Block, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -1.47141 0.25186 65.20283 -5.842 1.79e-07 ***
HabitatVegetated 0.52673 0.31273 229.69739 1.684 0.0935 .
TreatmentBiomass Removal 3.16743 0.24299 230.30671 13.035 < 2e-16 ***
TreatmentHemizonia -0.05973 0.31385 230.17407 -0.190 0.8492
TreatmentRaking 0.25499 0.41045 230.89163 0.621 0.5351
TreatmentRaking & Clipping 0.54463 0.31430 230.49078 1.733 0.0845 .
HabitatVegetated:TreatmentBiomass Removal -0.59959 0.35315 229.14496 -1.698 0.0909 .
HabitatVegetated:TreatmentHemizonia -0.30595 0.44325 229.12563 -0.690 0.4907
HabitatVegetated:TreatmentRaking -0.20398 0.55605 228.65197 -0.367 0.7141
HabitatVegetated:TreatmentRaking & Clipping -0.79088 0.43203 229.90026 -1.831 0.0685 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.564
TrtmntBmssR -0.738 0.582
TretmntHmzn -0.558 0.439 0.582
TretmntRkng -0.445 0.351 0.463 0.342
TrtmntRkn&C -0.570 0.455 0.591 0.450 0.361
HbttVgt:TBR 0.500 -0.883 -0.675 -0.390 -0.309 -0.403
HbttVgtt:TH 0.392 -0.702 -0.405 -0.690 -0.246 -0.316 0.619
HbttVgtt:TR 0.319 -0.555 -0.331 -0.252 -0.723 -0.258 0.490 0.391
HbttVg:TR&C 0.413 -0.727 -0.425 -0.316 -0.262 -0.725 0.641 0.507 0.405anova(fullmodel3)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Habitat 0.84 0.844 1 229.78 1.0140 0.3150
Treatment 483.49 120.873 4 231.54 145.1817 <2e-16 ***
Habitat:Treatment 3.83 0.959 4 229.30 1.1514 0.3332
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1###QQ Plots Now we’ll look at the QQ plots and the residuals using DHARMa
qqnorm(resid(fullmodel3)) #qqplot
qqline(resid(fullmodel3)) #add the line
testDispersion(fullmodel3) #red line should be in the middle of the distribution
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.9764, p-value = 0.904
alternative hypothesis: two.sided
myDHARMagraph3<-simulateResiduals(fullmodel3) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph3) #plotting graph. At this point, you don't want any text or lines to be red.
When we compare the model summaries and Anova of Models 2 & 3, we see that the removal of Site doesn’t impact the model. Therefore the more simple Model 3 is a better choice. But let’s keep checking…
###Compare AIC Scores Now I need to compare the AIC scores for all the models to tell me which is the better model (but it will not say which fits my data better, that is why I did all the DHARMa stuff)
Using aictab to make the comparison of models and table
models<-list(fullmodel1,fullmodel2,fullmodel3)
mod.names<-c('Site.Random','Site.Fixed','No.Site')
aictab(cand.set=models,modnames=mod.names)Warning:
Model selection for fixed effects is only appropriate with ML estimation:
REML (default) should only be used to select random effects for a constant set of fixed effects
Model selection based on AICc:
K AICc Delta_AICc AICcWt Cum.Wt Res.LL
No.Site 12 700.60 0.00 0.75 0.75 -337.63
Site.Random 13 702.82 2.23 0.25 1.00 -337.63
Site.Fixed 19 719.64 19.04 0.00 1.00 -339.15#The lowest AICc score is listed first and indicates the best fitting model, here, Model 3 (No.Site) where Site is not included. The cut-off for comparing Models is 2 units. The difference between Model 1 (Site.Random) and Model 3 (No.Site) is 2.12. So Model 3 is marginally better than Model 2.##Other Models Attempted Other ideas that were explored to keep Site but resulted in singular error and Site explaining 0.000 of the variance: fullmodel4<-lmer(log(Biomass)~ Treatment + (1|Site) + (1|Block), data = mydata) summary(fullmodel4) fullmodel5<-lmer(log(Biomass)~ Treatment + Habitat + (1|Site) + (1|Block), data = mydata) summary(fullmodel5)
###Best Model
fullmodel3<-lmer(log(Biomass)~
Habitat*Treatment+
(1|Block),
data=reproduction_mydata)
summary(fullmodel3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + (1 | Block)
Data: reproduction_mydata
REML criterion at convergence: 675.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2235 -0.6825 0.0572 0.6111 2.9658
Random effects:
Groups Name Variance Std.Dev.
Block (Intercept) 0.1839 0.4288
Residual 0.8326 0.9124
Number of obs: 247, groups: Block, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -1.47141 0.25186 65.20283 -5.842 1.79e-07 ***
HabitatVegetated 0.52673 0.31273 229.69739 1.684 0.0935 .
TreatmentBiomass Removal 3.16743 0.24299 230.30671 13.035 < 2e-16 ***
TreatmentHemizonia -0.05973 0.31385 230.17407 -0.190 0.8492
TreatmentRaking 0.25499 0.41045 230.89163 0.621 0.5351
TreatmentRaking & Clipping 0.54463 0.31430 230.49078 1.733 0.0845 .
HabitatVegetated:TreatmentBiomass Removal -0.59959 0.35315 229.14496 -1.698 0.0909 .
HabitatVegetated:TreatmentHemizonia -0.30595 0.44325 229.12563 -0.690 0.4907
HabitatVegetated:TreatmentRaking -0.20398 0.55605 228.65197 -0.367 0.7141
HabitatVegetated:TreatmentRaking & Clipping -0.79088 0.43203 229.90026 -1.831 0.0685 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.564
TrtmntBmssR -0.738 0.582
TretmntHmzn -0.558 0.439 0.582
TretmntRkng -0.445 0.351 0.463 0.342
TrtmntRkn&C -0.570 0.455 0.591 0.450 0.361
HbttVgt:TBR 0.500 -0.883 -0.675 -0.390 -0.309 -0.403
HbttVgtt:TH 0.392 -0.702 -0.405 -0.690 -0.246 -0.316 0.619
HbttVgtt:TR 0.319 -0.555 -0.331 -0.252 -0.723 -0.258 0.490 0.391
HbttVg:TR&C 0.413 -0.727 -0.425 -0.316 -0.262 -0.725 0.641 0.507 0.405###Post-Hoc Test You should remove non-significant interaction terms before running a post-hoc test. It is difficult to judge the main effects (Habitat and Site) when you also have the interaction term present, so when it is not significant, fit a new model without it.
fullmodel3.1<-lmer(log(Biomass)~
Treatment+
(1|Block),
data=reproduction_mydata)
summary(fullmodel3.1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Treatment + (1 | Block)
Data: reproduction_mydata
REML criterion at convergence: 677
Scaled residuals:
Min 1Q Median 3Q Max
-3.1914 -0.6482 0.0311 0.6048 3.1267
Random effects:
Groups Name Variance Std.Dev.
Block (Intercept) 0.1819 0.4265
Residual 0.8320 0.9121
Number of obs: 247, groups: Block, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -1.2310 0.2074 33.6195 -5.936 1.09e-06 ***
TreatmentBiomass Removal 2.8917 0.1790 236.6910 16.159 < 2e-16 ***
TreatmentHemizonia -0.1744 0.2261 237.1867 -0.772 0.441
TreatmentRaking 0.1873 0.2822 237.0309 0.664 0.508
TreatmentRaking & Clipping 0.1447 0.2140 235.2223 0.676 0.500
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) TrtmBR TrtmnH TrtmnR
TrtmntBmssR -0.673
TretmntHmzn -0.527 0.616
TretmntRkng -0.428 0.500 0.368
TrtmntRkn&C -0.553 0.644 0.516 0.412anova(fullmodel3.1)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Treatment 490.29 122.57 4 236.51 147.33 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1qqnorm(resid(fullmodel3.1)) #qqplot
qqline(resid(fullmodel3.1)) #add the line
testDispersion(fullmodel3.1) #red line should be in the middle of the distribution
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.99335, p-value = 0.896
alternative hypothesis: two.sided
myDHARMagraph3.1<-simulateResiduals(fullmodel3.1) #testing for heteroscedasticity
plot(myDHARMagraph3.1) #plotting graph
fullmodel3.2<-lmer(log(Biomass)~
Treatment+
(1|Block)+
(1|Population),
data=mydata)
summary(fullmodel3.2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Treatment + (1 | Block) + (1 | Population)
Data: mydata
REML criterion at convergence: 1381.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.4116 -0.6367 -0.0001 0.6389 3.6745
Random effects:
Groups Name Variance Std.Dev.
Population (Intercept) 0.000801 0.0283
Block (Intercept) 0.060607 0.2462
Residual 1.290867 1.1362
Number of obs: 441, groups: Population, 16; Block, 10
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.0644 0.1565 52.0231 -13.195 <2e-16 ***
TreatmentBiomass Removal 3.5659 0.1650 426.7907 21.609 <2e-16 ***
TreatmentHemizonia -0.0984 0.1952 432.3439 -0.504 0.6145
TreatmentRaking -0.2757 0.1920 428.3744 -1.436 0.1517
TreatmentRaking & Clipping 0.3912 0.1822 428.2538 2.147 0.0324 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) TrtmBR TrtmnH TrtmnR
TrtmntBmssR -0.712
TretmntHmzn -0.602 0.571
TretmntRkng -0.608 0.577 0.484
TrtmntRkn&C -0.641 0.607 0.519 0.519anova(fullmodel3.2)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Treatment 1233 308.25 4 426.32 238.79 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1qqnorm(resid(fullmodel3.2)) #qqplot
qqline(resid(fullmodel3.2)) #add the line
testDispersion(fullmodel3.2) #red line should be in the middle of the distribution
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.98522, p-value = 0.904
alternative hypothesis: two.sided
myDHARMagraph3.2<-simulateResiduals(fullmodel3.2) #testing for heteroscedasticity
plot(myDHARMagraph3.2) #plotting graph
Let’s use emmeans for our Best Model (fullmodel3) and the two off-shoots.
#?emmeans, emmeans(model, pairwise ~ treatment)
emmeans(fullmodel3,pairwise~Treatment)NOTE: Results may be misleading due to involvement in interactions$emmeans
Treatment emmean SE df lower.CL upper.CL
Grassland -1.21 0.209 33.3 -1.63 -0.784
Biomass Removal 1.66 0.159 11.9 1.31 2.006
Hemizonia -1.42 0.213 35.1 -1.85 -0.989
Raking -1.06 0.272 80.4 -1.60 -0.514
Raking & Clipping -1.06 0.202 29.9 -1.47 -0.646
Results are averaged over the levels of: Habitat
Degrees-of-freedom method: kenward-roger
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Grassland - Biomass Removal -2.8676 0.180 232 -15.931 <.0001
Grassland - Hemizonia 0.2127 0.228 232 0.934 0.8833
Grassland - Raking -0.1530 0.285 232 -0.537 0.9834
Grassland - Raking & Clipping -0.1492 0.217 230 -0.687 0.9592
Biomass Removal - Hemizonia 3.0803 0.183 233 16.789 <.0001
Biomass Removal - Raking 2.7146 0.249 231 10.886 <.0001
Biomass Removal - Raking & Clipping 2.7184 0.172 231 15.811 <.0001
Hemizonia - Raking -0.3657 0.292 234 -1.251 0.7211
Hemizonia - Raking & Clipping -0.3619 0.220 231 -1.645 0.4700
Raking - Raking & Clipping 0.0038 0.279 231 0.014 1.0000
Results are averaged over the levels of: Habitat
Degrees-of-freedom method: kenward-roger
Results are given on the log (not the response) scale.
P value adjustment: tukey method for comparing a family of 5 estimates emmeans(fullmodel3.1,pairwise~Treatment)$emmeans
Treatment emmean SE df lower.CL upper.CL
Grassland -1.23 0.208 33.4 -1.65 -0.809
Biomass Removal 1.66 0.158 12.0 1.32 2.006
Hemizonia -1.41 0.212 35.4 -1.84 -0.976
Raking -1.04 0.270 80.2 -1.58 -0.507
Raking & Clipping -1.09 0.199 29.0 -1.49 -0.678
Degrees-of-freedom method: kenward-roger
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Grassland - Biomass Removal -2.8917 0.179 237 -16.126 <.0001
Grassland - Hemizonia 0.1744 0.227 237 0.770 0.9391
Grassland - Raking -0.1873 0.283 237 -0.662 0.9642
Grassland - Raking & Clipping -0.1447 0.214 235 -0.675 0.9616
Biomass Removal - Hemizonia 3.0661 0.183 238 16.756 <.0001
Biomass Removal - Raking 2.7044 0.248 236 10.916 <.0001
Biomass Removal - Raking & Clipping 2.7469 0.169 236 16.249 <.0001
Hemizonia - Raking -0.3617 0.291 239 -1.245 0.7249
Hemizonia - Raking & Clipping -0.3192 0.217 236 -1.470 0.5829
Raking - Raking & Clipping 0.0425 0.276 236 0.154 0.9999
Degrees-of-freedom method: kenward-roger
Results are given on the log (not the response) scale.
P value adjustment: tukey method for comparing a family of 5 estimates emmeans(fullmodel3.2,pairwise~Treatment)$emmeans
Treatment emmean SE df lower.CL upper.CL
Grassland -2.06 0.157 57.5 -2.38 -1.75
Biomass Removal 1.50 0.122 23.1 1.25 1.75
Hemizonia -2.16 0.161 62.1 -2.49 -1.84
Raking -2.34 0.158 58.9 -2.66 -2.02
Raking & Clipping -1.67 0.146 44.0 -1.97 -1.38
Degrees-of-freedom method: kenward-roger
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Grassland - Biomass Removal -3.5659 0.166 428 -21.525 <.0001
Grassland - Hemizonia 0.0984 0.196 433 0.501 0.9873
Grassland - Raking 0.2757 0.193 429 1.429 0.6092
Grassland - Raking & Clipping -0.3912 0.183 429 -2.136 0.2067
Biomass Removal - Hemizonia 3.6643 0.170 428 21.602 <.0001
Biomass Removal - Raking 3.8416 0.166 426 23.076 <.0001
Biomass Removal - Raking & Clipping 3.1746 0.155 423 20.486 <.0001
Hemizonia - Raking 0.1773 0.198 433 0.896 0.8985
Hemizonia - Raking & Clipping -0.4896 0.186 427 -2.631 0.0666
Raking - Raking & Clipping -0.6669 0.184 423 -3.620 0.0030
Degrees-of-freedom method: kenward-roger
Results are given on the log (not the response) scale.
P value adjustment: tukey method for comparing a family of 5 estimates #These models result in similar Tukey outcomes##ggplot - lmer
biomass_gg<-ggplot(data=reproduction_mydata,
aes(x=factor(Treatment,levels=c("Grassland","Biomass Removal","Raking","Raking & Clipping","Hemizonia")),
y=log(Biomass)))+
scale_y_continuous(expression(atop(italic("Dittrichia graveolens")~biomass, (log[10](mg)))))+
xlab("Treatment")+
ggtitle("")+
theme_classic(base_size=20)+
geom_boxplot() #plot data from log(data)
biomass_gg
ggsave(plot=biomass_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/biomass_gg.png",width=25,height=15,units="cm",dpi=800)
ggplot(data=reproduction_mydata,
aes(x=Treatment,
y=log(Biomass)))+
geom_boxplot() #plot data from log(data)
ggplot(data=reproduction_mydata,
aes(x=Habitat,
y=log(Biomass)))+
geom_boxplot() #plot data from log(data)
#Survival Analysis This code uses NumDaysAlive data with Habitat (roadside and vegetated) and Treatment in a Cox proportional hazards model to assess survival. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. “Surv” creates a survival object to combine the days column (NumDaysAlive) and the reproductive censor column (ReproductionCensor) to be used as a response variable in a model formula. The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: “var1 * var2” will give you the interaction term and the individual variables: so “var1 + var2 + var1:var2”. To add random effects, type “+ (1|random effect)”.
##Histograms
#Number of Days Alive - All data
hist(mydata$NumDaysAlive,col='steelblue',main='Original') 
#Number of Days Alive - By Habitat
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Habitat)) #Here we see that both Habitats have the same bi-modal distribution.
#Number of Days Alive - By Treatment
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that 4 of the treatments have the same bi-modal distribution and Biomass Removal is right skewed.
##Cox Model Start by making a simple model with no random effects. This will be compared to the full model with random effects.
cox_simplemodel1<-coxph(Surv(NumDaysAlive,
CensorReproduction)~
Habitat*Treatment,
data=mydata)
summary(cox_simplemodel1)Call:
coxph(formula = Surv(NumDaysAlive, CensorReproduction) ~ Habitat *
Treatment, data = mydata)
n= 800, number of events= 253
coef exp(coef) se(coef) z Pr(>|z|)
HabitatVegetated -0.5980 0.5499 0.3308 -1.808 0.0706 .
TreatmentBiomass Removal 1.2401 3.4559 0.2771 4.475 7.65e-06 ***
TreatmentHemizonia 0.1563 1.1692 0.3364 0.465 0.6422
TreatmentRaking -0.8104 0.4447 0.4404 -1.840 0.0657 .
TreatmentRaking & Clipping 0.3718 1.4504 0.3304 1.125 0.2604
HabitatVegetated:TreatmentBiomass Removal 0.5848 1.7946 0.3772 1.550 0.1210
HabitatVegetated:TreatmentHemizonia 0.2799 1.3229 0.4740 0.590 0.5549
HabitatVegetated:TreatmentRaking 1.1347 3.1102 0.5861 1.936 0.0529 .
HabitatVegetated:TreatmentRaking & Clipping 0.8470 2.3327 0.4581 1.849 0.0645 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
HabitatVegetated 0.5499 1.8185 0.2875 1.052
TreatmentBiomass Removal 3.4559 0.2894 2.0076 5.949
TreatmentHemizonia 1.1692 0.8553 0.6047 2.261
TreatmentRaking 0.4447 2.2489 0.1876 1.054
TreatmentRaking & Clipping 1.4504 0.6895 0.7590 2.771
HabitatVegetated:TreatmentBiomass Removal 1.7946 0.5572 0.8569 3.758
HabitatVegetated:TreatmentHemizonia 1.3229 0.7559 0.5224 3.350
HabitatVegetated:TreatmentRaking 3.1102 0.3215 0.9861 9.809
HabitatVegetated:TreatmentRaking & Clipping 2.3327 0.4287 0.9504 5.725
Concordance= 0.604 (se = 0.024 )
Likelihood ratio test= 90.68 on 9 df, p=1e-15
Wald test = 77.28 on 9 df, p=6e-13
Score (logrank) test = 87.13 on 9 df, p=6e-15print(cox_simplemodel1)Call:
coxph(formula = Surv(NumDaysAlive, CensorReproduction) ~ Habitat *
Treatment, data = mydata)
coef exp(coef) se(coef) z p
HabitatVegetated -0.5980 0.5499 0.3308 -1.808 0.0706
TreatmentBiomass Removal 1.2401 3.4559 0.2771 4.475 7.65e-06
TreatmentHemizonia 0.1563 1.1692 0.3364 0.465 0.6422
TreatmentRaking -0.8104 0.4447 0.4404 -1.840 0.0657
TreatmentRaking & Clipping 0.3718 1.4504 0.3304 1.125 0.2604
HabitatVegetated:TreatmentBiomass Removal 0.5848 1.7946 0.3772 1.550 0.1210
HabitatVegetated:TreatmentHemizonia 0.2799 1.3229 0.4740 0.590 0.5549
HabitatVegetated:TreatmentRaking 1.1347 3.1102 0.5861 1.936 0.0529
HabitatVegetated:TreatmentRaking & Clipping 0.8470 2.3327 0.4581 1.849 0.0645
Likelihood ratio test=90.68 on 9 df, p=1.187e-15
n= 800, number of events= 253 predict(cox_simplemodel1) [1] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[10] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[19] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[28] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[37] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[46] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[55] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[64] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817
[73] 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2400817 1.2268298
[82] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[91] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[100] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[109] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[118] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[127] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[136] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[145] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298
[154] 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 1.2268298 0.0000000 0.0000000
[163] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[172] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[181] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[190] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[199] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[208] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[217] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[226] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[235] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -0.5980207 -0.5980207 -0.5980207
[244] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[253] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[262] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[271] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[280] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[289] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[298] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[307] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[316] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 0.1563201 0.1563201 0.1563201 0.1563201
[325] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[334] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[343] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[352] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[361] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[370] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[379] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[388] 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201 0.1563201
[397] 0.1563201 0.1563201 0.1563201 0.1563201 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[406] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[415] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[424] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[433] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[442] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[451] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[460] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[469] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[478] -0.1618398 -0.1618398 -0.1618398 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[487] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[496] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[505] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[514] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[523] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[532] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[541] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[550] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[559] -0.8104351 -0.8104351 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[568] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[577] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[586] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[595] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[604] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[613] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[622] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[631] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[640] -0.2737686 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[649] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[658] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[667] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[676] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[685] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[694] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[703] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[712] 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368 0.3718368
[721] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[730] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[739] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[748] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[757] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[766] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[775] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[784] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270
[793] 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270 0.6208270hist(predict(cox_simplemodel1))
Now make a full model using random effects
cox_fullmodel1<-coxme(Surv(NumDaysAlive,CensorReproduction)~
Habitat*Treatment+
(1|Site)+
(1|Block),
data=mydata)
summary(cox_fullmodel1)Cox mixed-effects model fit by maximum likelihood
Data: mydata
events, n = 253, 800
Iterations= 19 119
NULL Integrated Fitted
Log-likelihood -1204.23 -1117.392 -1093.286
Chisq df p AIC BIC
Integrated loglik 173.68 11.00 0 151.68 112.81
Penalized loglik 221.89 22.42 0 177.04 97.81
Model: Surv(NumDaysAlive, CensorReproduction) ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Fixed coefficients
coef exp(coef) se(coef) z p
HabitatVegetated -0.37919954 0.6844090 0.3444499 -1.10 2.7e-01
TreatmentBiomass Removal 1.38647395 4.0007184 0.2911902 4.76 1.9e-06
TreatmentHemizonia -0.09751812 0.9070859 0.3540413 -0.28 7.8e-01
TreatmentRaking -0.64329505 0.5255578 0.4572637 -1.41 1.6e-01
TreatmentRaking & Clipping 0.59622662 1.8152562 0.3498383 1.70 8.8e-02
HabitatVegetated:TreatmentBiomass Removal 0.45521372 1.5765103 0.3874747 1.17 2.4e-01
HabitatVegetated:TreatmentHemizonia 0.25268759 1.2874810 0.4903683 0.52 6.1e-01
HabitatVegetated:TreatmentRaking 0.80143838 2.2287444 0.6000419 1.34 1.8e-01
HabitatVegetated:TreatmentRaking & Clipping 0.68420207 1.9821896 0.4766792 1.44 1.5e-01
Random effects
Group Variable Std Dev Variance
Site Intercept 0.29585965 0.08753293
Block Intercept 0.83465076 0.69664189print(cox_fullmodel1)Cox mixed-effects model fit by maximum likelihood
Data: mydata
events, n = 253, 800
Iterations= 19 119
NULL Integrated Fitted
Log-likelihood -1204.23 -1117.392 -1093.286
Chisq df p AIC BIC
Integrated loglik 173.68 11.00 0 151.68 112.81
Penalized loglik 221.89 22.42 0 177.04 97.81
Model: Surv(NumDaysAlive, CensorReproduction) ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Fixed coefficients
coef exp(coef) se(coef) z p
HabitatVegetated -0.37919954 0.6844090 0.3444499 -1.10 2.7e-01
TreatmentBiomass Removal 1.38647395 4.0007184 0.2911902 4.76 1.9e-06
TreatmentHemizonia -0.09751812 0.9070859 0.3540413 -0.28 7.8e-01
TreatmentRaking -0.64329505 0.5255578 0.4572637 -1.41 1.6e-01
TreatmentRaking & Clipping 0.59622662 1.8152562 0.3498383 1.70 8.8e-02
HabitatVegetated:TreatmentBiomass Removal 0.45521372 1.5765103 0.3874747 1.17 2.4e-01
HabitatVegetated:TreatmentHemizonia 0.25268759 1.2874810 0.4903683 0.52 6.1e-01
HabitatVegetated:TreatmentRaking 0.80143838 2.2287444 0.6000419 1.34 1.8e-01
HabitatVegetated:TreatmentRaking & Clipping 0.68420207 1.9821896 0.4766792 1.44 1.5e-01
Random effects
Group Variable Std Dev Variance
Site Intercept 0.29585965 0.08753293
Block Intercept 0.83465076 0.69664189predict(cox_fullmodel1) [1] 3.412069248 1.723904182 1.142354944 1.411114662 2.826926974 1.710423900 1.521797855 1.543482099
[9] 1.020912356 0.832419259 3.057516702 1.369351636 0.787802397 1.056562116 2.472374428 1.355871354
[17] 1.167245309 1.188929553 0.666359810 0.477866713 3.027300084 1.339135018 0.757585779 1.026345498
[25] 2.442157809 1.325654736 1.137028691 1.158712935 0.636143192 0.447650095 3.111607266 1.423442200
[33] 0.841892961 1.110652680 2.526464992 1.409961918 1.221335873 1.243020117 0.720450374 0.531957277
[41] 2.783497956 1.095332890 0.513783652 0.782543370 2.198355682 1.081852608 0.893226563 0.914910807
[49] 0.392341064 0.203847967 3.491439676 1.803274610 1.221725371 1.490485090 2.906297402 1.789794328
[57] 1.601168283 1.622852527 1.100282784 0.911789687 2.874174708 1.186009641 0.604460403 0.873220121
[65] 2.289032433 1.172529359 0.983903315 1.005587559 0.483017815 0.294524719 2.914415579 1.226250512
[73] 0.644701274 0.913460992 2.329273304 1.212770231 1.024144186 1.045828430 0.523258686 0.334765590
[81] 3.488083434 1.799918367 1.218369129 1.487128847 2.902941159 1.786438086 1.597812041 1.619496285
[89] 1.096926541 0.908433445 3.133530888 1.445365821 0.863816583 1.132576301 2.548388613 1.431885540
[97] 1.243259495 1.264943739 0.742373995 0.553880899 3.103314270 1.415149203 0.833599965 1.102359683
[105] 2.518171995 1.401668921 1.213042877 1.234727121 0.712157377 0.523664281 3.187621452 1.499456385
[113] 0.917907147 1.186666865 2.602479177 1.485976104 1.297350059 1.319034303 0.796464559 0.607971463
[121] 2.859512142 1.171347075 0.589797837 0.858557555 2.274369867 1.157866794 0.969240749 0.990924993
[129] 0.468355249 0.279862153 3.567453862 1.879288795 1.297739557 1.566499275 2.982311587 1.865808513
[137] 1.677182469 1.698866713 1.176296969 0.987803873 2.950188893 1.262023827 0.680474588 0.949234307
[145] 2.365046619 1.248543545 1.059917500 1.081601744 0.559032001 0.370538904 2.990429765 1.302264698
[153] 0.720715460 0.989475178 2.405287490 1.288784416 1.100158372 1.121842616 0.599272872 0.410779776
[161] 2.025595296 0.337430230 -0.244119009 0.024640710 1.440453022 0.323949948 0.135323903 0.157008147
[169] -0.365561596 -0.554054693 1.671042750 -0.017122316 -0.598671555 -0.329911836 1.085900476 -0.030602598
[177] -0.219228643 -0.197544399 -0.720114142 -0.908607239 1.640826132 -0.047338935 -0.628888173 -0.360128455
[185] 1.055683857 -0.060819216 -0.249445261 -0.227761017 -0.750330761 -0.938823857 1.725133314 0.036968248
[193] -0.544580991 -0.275821272 1.139991040 0.023487966 -0.165138079 -0.143453835 -0.666023578 -0.854516675
[201] 1.397024004 -0.291141062 -0.872690301 -0.603930582 0.811881730 -0.304621344 -0.493247389 -0.471563145
[209] -0.994132888 -1.182625985 2.104965724 0.416800657 -0.164748581 0.104011137 1.519823449 0.403320376
[217] 0.214694331 0.236378575 -0.286191169 -0.474684265 1.487700756 -0.200464311 -0.782013549 -0.513253831
[225] 0.902558481 -0.213944593 -0.402570638 -0.380886393 -0.903456137 -1.091949233 1.527941627 -0.160223440
[233] -0.741772678 -0.473012960 0.942799352 -0.173703721 -0.362329766 -0.340645522 -0.863215266 -1.051708362
[241] 1.646395758 -0.041769308 -0.623318547 -0.354558828 1.061253484 -0.055249590 -0.243875635 -0.222191391
[249] -0.744761134 -0.933254231 1.291843212 -0.396321855 -0.977871093 -0.709111374 0.706700937 -0.409802136
[257] -0.598428181 -0.576743937 -1.099313681 -1.287806777 1.261626594 -0.426538473 -1.008087711 -0.739327993
[265] 0.676484319 -0.440018754 -0.628644799 -0.606960555 -1.129530299 -1.318023395 1.345933776 -0.342231290
[273] -0.923780529 -0.655020810 0.760791502 -0.355711572 -0.544337617 -0.522653373 -1.045223116 -1.233716213
[281] 1.017824466 -0.670340600 -1.251889839 -0.983130120 0.432682192 -0.683820882 -0.872446927 -0.850762683
[289] -1.373332426 -1.561825523 1.725766186 0.037601119 -0.543948119 -0.275188401 1.140623911 0.024120838
[297] -0.164505207 -0.142820963 -0.665390707 -0.853883803 1.108501217 -0.579663849 -1.161213087 -0.892453369
[305] 0.523358943 -0.593144131 -0.781770176 -0.760085932 -1.282655675 -1.471148772 1.148742089 -0.539422978
[313] -1.120972216 -0.852212498 0.563599814 -0.552903260 -0.741529304 -0.719845060 -1.242414804 -1.430907900
[321] 1.928077178 0.239912111 -0.341637127 -0.072877409 1.342934903 0.226431829 0.037805785 0.059490029
[329] -0.463079715 -0.651572811 1.573524632 -0.114640435 -0.696189673 -0.427429955 0.988382357 -0.128120717
[337] -0.316746761 -0.295062517 -0.817632261 -1.006125357 1.543308014 -0.144857053 -0.726406291 -0.457646573
[345] 0.958165739 -0.158337335 -0.346963380 -0.325279135 -0.847848879 -1.036341975 1.627615196 -0.060549871
[353] -0.642099109 -0.373339391 1.042472921 -0.074030153 -0.262656197 -0.240971953 -0.763541697 -0.952034793
[361] 1.299505886 -0.388659181 -0.970208419 -0.701448701 0.714363611 -0.402139463 -0.590765507 -0.569081263
[369] -1.091651007 -1.280144103 2.007447606 0.319282539 -0.262266699 0.006493019 1.422305331 0.305802257
[377] 0.117176212 0.138860457 -0.383709287 -0.572202383 1.390182637 -0.297982430 -0.879531668 -0.610771950
[385] 0.805040362 -0.311462711 -0.500088756 -0.478404512 -1.000974256 -1.189467352 1.430423508 -0.257741558
[393] -0.839290797 -0.570531078 0.845281234 -0.271221840 -0.459847885 -0.438163641 -0.960733384 -1.149226481
[401] 1.801565225 0.113400159 -0.468149079 -0.199389361 1.216422951 0.099919877 -0.088706168 -0.067021924
[409] -0.589591667 -0.778084764 1.447012679 -0.241152387 -0.822701626 -0.553941907 0.861870405 -0.254632669
[417] -0.443258714 -0.421574470 -0.944144213 -1.132637310 1.416796061 -0.271369005 -0.852918244 -0.584158525
[425] 0.831653787 -0.284849287 -0.473475332 -0.451791088 -0.974360831 -1.162853928 1.501103243 -0.187061823
[433] -0.768611061 -0.499851343 0.915960969 -0.200542105 -0.389168150 -0.367483906 -0.890053649 -1.078546746
[441] 1.172993933 -0.515171133 -1.096720371 -0.827960653 0.587851659 -0.528651415 -0.717277460 -0.695593216
[449] -1.218162959 -1.406656056 1.880935653 0.192770587 -0.388778652 -0.120018933 1.295793379 0.179290305
[457] -0.009335740 0.012348504 -0.510221239 -0.698714336 1.263670685 -0.424494382 -1.006043620 -0.737283902
[465] 0.678528410 -0.437974664 -0.626600708 -0.604916464 -1.127486208 -1.315979304 1.303911556 -0.384253511
[473] -0.965802749 -0.697043031 0.718769281 -0.397733792 -0.586359837 -0.564675593 -1.087245337 -1.275738433
[481] 1.382300247 -0.305864820 -0.887414058 -0.618654340 0.797157972 -0.319345102 -0.507971146 -0.486286902
[489] -1.008856646 -1.197349742 1.027747700 -0.660417366 -1.241966604 -0.973206886 0.442605426 -0.673897648
[497] -0.862523693 -0.840839449 -1.363409192 -1.551902288 0.997531082 -0.690633984 -1.272183223 -1.003423504
[505] 0.412388808 -0.704114266 -0.892740311 -0.871056067 -1.393625810 -1.582118907 1.081838265 -0.606326802
[513] -1.187876040 -0.919116322 0.496695990 -0.619807084 -0.808433128 -0.786748884 -1.309318628 -1.497811724
[521] 0.753728955 -0.934436112 -1.515985350 -1.247225632 0.168586680 -0.947916394 -1.136542438 -1.114858194
[529] -1.637427938 -1.825921034 1.461670674 -0.226494392 -0.808043631 -0.539283912 0.876528400 -0.239974674
[537] -0.428600719 -0.406916475 -0.929486218 -1.117979315 0.844405706 -0.843759361 -1.425308599 -1.156548881
[545] 0.259263431 -0.857239643 -1.045865687 -1.024181443 -1.546751187 -1.735244283 0.884646577 -0.803518489
[553] -1.385067728 -1.116308009 0.299504303 -0.816998771 -1.005624816 -0.983940572 -1.506510315 -1.695003412
[561] 1.804539087 0.116374020 -0.465175218 -0.196415500 1.219396812 0.102893738 -0.085732307 -0.064048062
[569] -0.586617806 -0.775110902 1.449986540 -0.238178526 -0.819727765 -0.550968046 0.864844266 -0.251658808
[577] -0.440284853 -0.418600609 -0.941170352 -1.129663449 1.419769922 -0.268395144 -0.849944383 -0.581184664
[585] 0.834627648 -0.281875426 -0.470501471 -0.448817227 -0.971386970 -1.159880067 1.504077104 -0.184087962
[593] -0.765637200 -0.496877482 0.918934830 -0.197568244 -0.386194289 -0.364510045 -0.887079788 -1.075572884
[601] 1.175967795 -0.512197272 -1.093746510 -0.824986792 0.590825520 -0.525677554 -0.714303599 -0.692619355
[609] -1.215189098 -1.403682194 1.883909514 0.195744448 -0.385804791 -0.117045072 1.298767240 0.182264166
[617] -0.006361879 0.015322365 -0.507247378 -0.695740475 1.266644546 -0.421520521 -1.003069759 -0.734310041
[625] 0.681502271 -0.435000803 -0.623626847 -0.601942603 -1.124512347 -1.313005443 1.306885417 -0.381279650
[633] -0.962828888 -0.694069170 0.721743142 -0.394759931 -0.583385976 -0.561701732 -1.084271476 -1.272764572
[641] 2.621821912 0.933656845 0.352107607 0.620867325 2.036679637 0.920176564 0.731550519 0.753234763
[649] 0.230665019 0.042171923 2.267269366 0.579104299 -0.002444939 0.266314779 1.682127091 0.565624018
[657] 0.376997973 0.398682217 -0.123887527 -0.312380623 2.237052748 0.548887681 -0.032661557 0.236098161
[665] 1.651910473 0.535407399 0.346781355 0.368465599 -0.154104145 -0.342597241 2.321359930 0.633194863
[673] 0.051645625 0.320405343 1.736217655 0.619714582 0.431088537 0.452772781 -0.069796963 -0.258290059
[681] 1.993250620 0.305085553 -0.276463685 -0.007703967 1.408108345 0.291605272 0.102979227 0.124663471
[689] -0.397906273 -0.586399369 2.701192340 1.013027273 0.431478035 0.700237753 2.116050065 0.999546991
[697] 0.810920947 0.832605191 0.310035447 0.121542351 2.083927371 0.395762305 -0.185786934 0.082972785
[705] 1.498785097 0.382282023 0.193655978 0.215340222 -0.307229521 -0.495722618 2.124168243 0.436003176
[713] -0.145546062 0.123213656 1.539025968 0.422522894 0.233896850 0.255581094 -0.266988650 -0.455481746
[721] 2.926824446 1.238659380 0.657110141 0.925869860 2.341682172 1.225179098 1.036553053 1.058237297
[729] 0.535667554 0.347174457 2.572271900 0.884106833 0.302557595 0.571317313 1.987129625 0.870626552
[737] 0.682000507 0.703684751 0.181115007 -0.007378089 2.542055282 0.853890215 0.272340977 0.541100695
[745] 1.956913007 0.840409934 0.651783889 0.673468133 0.150898389 -0.037594707 2.626362464 0.938197398
[753] 0.356648159 0.625407878 2.041220189 0.924717116 0.736091071 0.757775315 0.235205572 0.046712475
[761] 2.298253154 0.610088088 0.028538849 0.297298568 1.713110880 0.596607806 0.407981761 0.429666005
[769] -0.092903738 -0.281396835 3.006194874 1.318029807 0.736480569 1.005240287 2.421052599 1.304549526
[777] 1.115923481 1.137607725 0.615037981 0.426544885 2.388929905 0.700764839 0.119215600 0.387975319
[785] 1.803787631 0.687284557 0.498658512 0.520342756 -0.002226987 -0.190720084 2.429170777 0.741005710
[793] 0.159456472 0.428216190 1.844028502 0.727525428 0.538899384 0.560583628 0.038013884 -0.150479212hist(predict(cox_fullmodel1))
Now we can compare the models to see which model is best
anova(cox_simplemodel1,cox_fullmodel1) Analysis of Deviance Table
Cox model: response is Surv(NumDaysAlive, CensorReproduction)
Model 1: ~Habitat * Treatment
Model 2: ~Habitat * Treatment + (1 | Site) + (1 | Block)
loglik Chisq Df P(>|Chi|)
1 -1158.9
2 -1117.4 82.993 2 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#See example: https://www.rdocumentation.org/packages/coxme/versions/2.2-16/topics/coxme
AIC(cox_simplemodel1,cox_fullmodel1) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here it is the fullmodel1.Now, let’s make a model with no interaction term
cox_fullmodel2<-coxme(Surv(NumDaysAlive,CensorReproduction)~
Habitat+
Treatment+
(1|Site)+
(1|Block),
data=mydata)
summary(cox_fullmodel2)Cox mixed-effects model fit by maximum likelihood
Data: mydata
events, n = 253, 800
Iterations= 18 95
NULL Integrated Fitted
Log-likelihood -1204.23 -1118.851 -1094.381
Chisq df p AIC BIC
Integrated loglik 170.76 7.00 0 156.76 132.02
Penalized loglik 219.70 18.58 0 182.53 116.86
Model: Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment + (1 | Site) + (1 | Block)
Fixed coefficients
coef exp(coef) se(coef) z p
HabitatVegetated 0.04968034 1.0509351 0.1326079 0.37 7.1e-01
TreatmentBiomass Removal 1.60910167 4.9983191 0.2268731 7.09 1.3e-12
TreatmentHemizonia 0.01272746 1.0128088 0.2465085 0.05 9.6e-01
TreatmentRaking -0.20667151 0.8132868 0.3019860 -0.68 4.9e-01
TreatmentRaking & Clipping 0.95534162 2.5995585 0.2426422 3.94 8.2e-05
Random effects
Group Variable Std Dev Variance
Site Intercept 0.30795467 0.09483608
Block Intercept 0.84503192 0.71407895Let’s compare the the first two models to test for the significance of the term that is removed (using LR)
anova(cox_fullmodel1,cox_fullmodel2) #Not significantAnalysis of Deviance Table
Cox model: response is Surv(NumDaysAlive, CensorReproduction)
Model 1: ~Habitat * Treatment + (1 | Site) + (1 | Block)
Model 2: ~Habitat + Treatment + (1 | Site) + (1 | Block)
loglik Chisq Df P(>|Chi|)
1 -1117.4
2 -1118.8 2.919 4 0.5715AIC(cox_fullmodel1,cox_fullmodel2) #Interaction term is not significant and the second model has a lower AIC score. So we can drop the interaction term and keep fullmodel2.So, now let’s add in population nested under site as a random effect
cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
Habitat+
Treatment+
(1|Site/Population)+
(1|Block),
data=mydata)
summary(cox_fullmodel3)Cox mixed-effects model fit by maximum likelihood
Data: mydata
events, n = 253, 800
Iterations= 20 105
NULL Integrated Fitted
Log-likelihood -1204.23 -1116.667 -1086.004
Chisq df p AIC BIC
Integrated loglik 175.13 8.00 0 159.13 130.86
Penalized loglik 236.45 22.68 0 191.10 110.96
Model: Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment + (1 | Site/Population) + (1 | Block)
Fixed coefficients
coef exp(coef) se(coef) z p
HabitatVegetated 0.03971413 1.0405133 0.2256647 0.18 8.6e-01
TreatmentBiomass Removal 1.58216404 4.8654735 0.2279166 6.94 3.9e-12
TreatmentHemizonia -0.03665974 0.9640041 0.2497694 -0.15 8.8e-01
TreatmentRaking -0.27759259 0.7576054 0.3053999 -0.91 3.6e-01
TreatmentRaking & Clipping 0.94413698 2.5705939 0.2466166 3.83 1.3e-04
Random effects
Group Variable Std Dev Variance
Site/Population (Intercept) 0.3669601665 0.1346597638
Site (Intercept) 0.0200515483 0.0004020646
Block Intercept 0.8757403665 0.7669211894Now we can compare the models to see which model is best
anova(cox_fullmodel2,cox_fullmodel3) #SignificantAnalysis of Deviance Table
Cox model: response is Surv(NumDaysAlive, CensorReproduction)
Model 1: ~Habitat + Treatment + (1 | Site) + (1 | Block)
Model 2: ~Habitat + Treatment + (1 | Site/Population) + (1 | Block)
loglik Chisq Df P(>|Chi|)
1 -1118.8
2 -1116.7 4.3685 1 0.03661 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1AIC(cox_fullmodel2,cox_fullmodel3) #Looks like fullmodel3 is the better model because of the lower AIC score###Best Model
cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
Habitat+
Treatment+
(1|Site/Population)+
(1|Block),
data=mydata)
summary(cox_fullmodel3)Cox mixed-effects model fit by maximum likelihood
Data: mydata
events, n = 253, 800
Iterations= 20 105
NULL Integrated Fitted
Log-likelihood -1204.23 -1116.667 -1086.004
Chisq df p AIC BIC
Integrated loglik 175.13 8.00 0 159.13 130.86
Penalized loglik 236.45 22.68 0 191.10 110.96
Model: Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment + (1 | Site/Population) + (1 | Block)
Fixed coefficients
coef exp(coef) se(coef) z p
HabitatVegetated 0.03971413 1.0405133 0.2256647 0.18 8.6e-01
TreatmentBiomass Removal 1.58216404 4.8654735 0.2279166 6.94 3.9e-12
TreatmentHemizonia -0.03665974 0.9640041 0.2497694 -0.15 8.8e-01
TreatmentRaking -0.27759259 0.7576054 0.3053999 -0.91 3.6e-01
TreatmentRaking & Clipping 0.94413698 2.5705939 0.2466166 3.83 1.3e-04
Random effects
Group Variable Std Dev Variance
Site/Population (Intercept) 0.3669601665 0.1346597638
Site (Intercept) 0.0200515483 0.0004020646
Block Intercept 0.8757403665 0.7669211894###Risk Assessment These values are found in the model summary, but if you want to pull them out, here is how you interpret them. 1 = no effect, <1 = decreased risk of death, >1 = increased risk of death.
exp(coef(cox_fullmodel3)) #This should be interpreted that Biomass Removal is almost 5% more likely to survive to reproduction compared to Control, and Raking + Clipping is about 2.5% more likely to survive to reproduction compared to Control. HabitatVegetated TreatmentBiomass Removal TreatmentHemizonia TreatmentRaking
1.0405133 4.8654735 0.9640041 0.7576054
TreatmentRaking & Clipping
2.5705939 exp(ranef(cox_fullmodel3)$Block) A B C D E F G H I J
5.9382701 1.0302514 0.5553219 0.7170038 3.1240541 1.0901227 0.7725668 0.8326539 0.4957931 0.3779511 exp(ranef(cox_fullmodel3)$Site) # Pretty even among all the sites BAY CHE GUA LEX OAP PAR PEN SSJ
1.0018910 0.9997332 0.9997487 1.0001393 0.9987233 1.0021430 0.9986960 0.9989318 Anova(cox_fullmodel3)Analysis of Deviance Table (Type II tests)
Response: Surv(NumDaysAlive, CensorReproduction)
Df Chisq Pr(>Chisq)
Habitat 1 0.031 0.8603
Treatment 4 84.060 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Looks like roadside and offroad plants are the same, so let’s combine them together in a model (aka, removing the Habitat term)
cox_fullmodel4<-coxme(Surv(NumDaysAlive,CensorReproduction)~
Treatment+
(1|Site/Population)+
(1|Block),
data=mydata)
summary(cox_fullmodel4)Cox mixed-effects model fit by maximum likelihood
Data: mydata
events, n = 253, 800
Iterations= 22 115
NULL Integrated Fitted
Log-likelihood -1204.23 -1116.683 -1086.019
Chisq df p AIC BIC
Integrated loglik 175.09 7.00 0 161.09 136.36
Penalized loglik 236.42 22.35 0 191.73 112.77
Model: Surv(NumDaysAlive, CensorReproduction) ~ Treatment + (1 | Site/Population) + (1 | Block)
Fixed coefficients
coef exp(coef) se(coef) z p
TreatmentBiomass Removal 1.5817104 4.8632667 0.2278906 6.94 3.9e-12
TreatmentHemizonia -0.0358444 0.9647904 0.2497418 -0.14 8.9e-01
TreatmentRaking -0.2777768 0.7574659 0.3054173 -0.91 3.6e-01
TreatmentRaking & Clipping 0.9445967 2.5717760 0.2466066 3.83 1.3e-04
Random effects
Group Variable Std Dev Variance
Site/Population (Intercept) 0.3670944471 0.1347583331
Site (Intercept) 0.0200501745 0.0004020095
Block Intercept 0.8752789365 0.7661132166summary(cox_fullmodel3)Cox mixed-effects model fit by maximum likelihood
Data: mydata
events, n = 253, 800
Iterations= 20 105
NULL Integrated Fitted
Log-likelihood -1204.23 -1116.667 -1086.004
Chisq df p AIC BIC
Integrated loglik 175.13 8.00 0 159.13 130.86
Penalized loglik 236.45 22.68 0 191.10 110.96
Model: Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment + (1 | Site/Population) + (1 | Block)
Fixed coefficients
coef exp(coef) se(coef) z p
HabitatVegetated 0.03971413 1.0405133 0.2256647 0.18 8.6e-01
TreatmentBiomass Removal 1.58216404 4.8654735 0.2279166 6.94 3.9e-12
TreatmentHemizonia -0.03665974 0.9640041 0.2497694 -0.15 8.8e-01
TreatmentRaking -0.27759259 0.7576054 0.3053999 -0.91 3.6e-01
TreatmentRaking & Clipping 0.94413698 2.5705939 0.2466166 3.83 1.3e-04
Random effects
Group Variable Std Dev Variance
Site/Population (Intercept) 0.3669601665 0.1346597638
Site (Intercept) 0.0200515483 0.0004020646
Block Intercept 0.8757403665 0.7669211894anova(cox_fullmodel3,cox_fullmodel4) #SignificantAnalysis of Deviance Table
Cox model: response is Surv(NumDaysAlive, CensorReproduction)
Model 1: ~Habitat + Treatment + (1 | Site/Population) + (1 | Block)
Model 2: ~Treatment + (1 | Site/Population) + (1 | Block)
loglik Chisq Df P(>|Chi|)
1 -1116.7
2 -1116.7 0.0319 1 0.8582AIC(cox_fullmodel3,cox_fullmodel4) #Looks like fullmodel4 is the better model because the AIC score is within 2 points of each other, therefore the models are assessed the same and you should take the simpler model. But this is for another manuscript, probably.##ggplot ###Interaction Plot
pd<-position_dodge(0)
surv_mydata1<-mydata%>%
filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
group_by(Treatment,Habitat)%>%
summarise(Mean=mean(PropBudSite),
SD=sd(PropBudSite),
N=length(PropBudSite))%>%
mutate(SE=SD/sqrt(N))`summarise()` has grouped output by 'Treatment'. You can override using the `.groups` argument.field_int_surv_all_gg<-ggplot(data=surv_mydata1,
aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
y=Mean,
shape=Habitat,
group=Habitat,
color=Habitat))+
ylab("Proportion Survival to Bud")+
coord_cartesian(ylim=c(0,1))+
xlab("")+
theme_classic(base_size=16)+
theme(panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.line=element_line(colour="black"),
legend.position = c(0.8, 0.8))+
scale_y_continuous(name="Proportion Survival to Bud",
limits=c(0,1),
breaks=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))+
geom_line(size=1,
position=pd)+
geom_point(size=5,
position=pd)+
scale_color_manual(values=c("gray85","forestgreen"))+
geom_errorbar(width=0.15,
position=pd,
aes(ymin=(Mean-SE),
ymax=(Mean+SE)))+
labs(y="Proportion Survival to Bud",
fill="Source Habitat",
color="Source Habitat",
shape="Source Habitat")
field_int_surv_all_gg
#ggsave(plot=field_int_surv_all_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_surv_all_gg.png",width=25,height=15,units="cm",dpi=800)
surv_mydata2<-mydata%>%
group_by(Treatment,Habitat)%>%
summarise(Mean=mean(PropBudSite),
SD=sd(PropBudSite),
N=length(PropBudSite))%>%
mutate(SE=SD/sqrt(N))`summarise()` has grouped output by 'Treatment'. You can override using the `.groups` argument.field_int_surv_all_gg<-ggplot(data=surv_mydata2,
aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland","Raking","Raking & Clipping","Hemizonia")),
y=Mean,
shape=Habitat,
group=Habitat,
color=Habitat))+
ylab("Proportion Survival to Bud")+
coord_cartesian(ylim=c(0,1))+
xlab("")+
theme_classic(base_size=16)+
theme(panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.line=element_line(colour="black"),
legend.position = c(0.8, 0.8))+
scale_y_continuous(name="Proportion Survival to Bud",
limits=c(0,1),
breaks=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))+
geom_line(size=1,
position=pd)+
geom_point(size=5,
position=pd)+
scale_color_manual(values=c("gray85","forestgreen"))+
geom_errorbar(width=0.15,
position=pd,
aes(ymin=(Mean-SE),
ymax=(Mean+SE)))+
labs(y="Proportion Survival to Bud",
fill="Source Habitat",
color="Source Habitat",
shape="Source Habitat")
field_int_surv_all_gg
survival_gg<-ggplot(data=mydata,
aes(x=factor(Treatment,levels=c("Grassland","Biomass Removal","Raking","Raking & Clipping","Hemizonia")),
y=as.numeric(PropBudSite)))+
labs(y="Proportion Survival to Bud",
x="Treatment")+
ggtitle("")+
theme_classic(base_size=20)+
geom_boxplot()
survival_gg
ggsave(plot=survival_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_gg.png",width=25,height=15,units="cm",dpi=800)
boxplot(mydata$PropBudSite~mydata$Treatment,xaxt="n",xlab=NA,ylab="Proportion Survival to Bud",las=1)
axis(1,at=seq(1,5,1),labels=c("Grassland","Biomass\nRemoval","Hemizonia","Raking","Raking &\nClipping"))
boxplot(mydata$PropBudSite~mydata$Treatment,ylab="Proportion Survival to Bud",las=1)
##autoplot - Survival Curves Plot by habitat test
```r
pd<-position_dodge(0)
cox_model_data1<-mydata%>%
filter(Treatment==\Biomass Removal\|Treatment==\Grassland\)
cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
Habitat,
data=cox_model_data1)
surv_curve_habitat_treat<-ggplot(data=cox_model_graph1)+
aes(x=time,
y=surv,
shape=strata,
group=strata,
color=strata)+
theme_classic(base_size=16)+
scale_y_continuous(name=\Survival Probability\,
limits=c(0,1),
breaks=seq(0.0,1.0,0.1))+
scale_x_continuous(name=\Survival Time (Days)\,
limits=c(0,275),
breaks=seq(0,275,20))+
geom_step(size=1,
position=pd)+
geom_point(size=5,
position=pd)+
scale_color_manual(values=c(\gray85\,\forestgreen\))+
labs(x=\Survival Time (Days)\,
y=\Survival Probability\)
surv_curve_habitat_treat
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<img 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" />
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##autoplot - Survival Curves
Plot by habitat
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
Habitat,
data=mydata)
autoplot(cox_model_graph1)+
labs(x=\\n Survival Time (Days)\,
y=\Survival Probabilities\n\,
title=\Survival Times Of \n Roadside and Vegetated Populations\n\)+
theme_bw(base_size=16)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,
size=12),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=12),
legend.title=element_text(face=\bold\,
size=10))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Plot by all treatment
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
cox_model_graph2<-survfit(Surv(NumDaysAlive,CensorReproduction)~
Treatment,
data=mydata)
survival_auto<-autoplot(cox_model_graph2)+
labs(x=\\n Survival Time (Days)\,
y=\Survival Probabilities\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
#scale_fill_discrete(name=\Treatment\)
survival_auto
ggsave(plot=survival_auto,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_auto.png\,width=25,height=15,units=\cm\,dpi=800)
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
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Post hoc Tukey
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc3VtbWFyeShnbGh0KGNveF9mdWxsbW9kZWw0LG1jcChUcmVhdG1lbnQ9XFxUdWtleVxcKSkpXG5gYGBcbmBgYCJ9 -->
```r
```r
summary(glht(cox_fullmodel4,mcp(Treatment=\Tukey\)))
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<!-- rnb-output-begin 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 -->
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: coxme(formula = Surv(NumDaysAlive, CensorReproduction) ~ Treatment + (1 | Site/Population) + (1 | Block), data = mydata)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|)
Biomass Removal - Grassland == 0 1.58171 0.22789 6.941 < 0.001
Hemizonia - Grassland == 0 -0.03584 0.24974 -0.144
0.99990
Raking - Grassland == 0 -0.27778 0.30542 -0.909 0.89129
Raking & Clipping - Grassland == 0 0.94460 0.24661 3.830 0.00117
Hemizonia - Biomass Removal == 0 -1.61755 0.23484 -6.888 <
0.001 Raking - Biomass Removal == 0 -1.85949 0.28678
-6.484 < 0.001 Raking & Clipping - Biomass Removal
== 0 -0.63711 0.21317 -2.989 0.02275
Raking - Hemizonia == 0 -0.24193 0.31280 -0.773 0.93699
Raking & Clipping - Hemizonia == 0 0.98044 0.25237 3.885 < 0.001
Raking & Clipping - Raking == 0 1.22237 0.29585 4.132
< 0.001 — Signif. codes: 0 ‘’ 0.001
‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported
– single-step method)
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Plot by 2 treatments (Control and Biomass Removal), and adding in Habitat
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```r
```r
cox_sub_mydata<-subset(mydata,Treatment%in%c('Control','Biomass Removal'))
cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
Treatment,
data=cox_sub_mydata)
autoplot(cox_model_graph3)+
labs(x=\\n Survival Time (Days)\,
y=\Survival Probabilities\n\,
title=\Survival Times Of \n Roadside and Vegetated Populations\n\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,
size=12),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=12),
legend.title=element_text(face=\bold\,
size=10))
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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
Treatment+
Habitat,
data=cox_sub_mydata)
autoplot(cox_model_graph3)+
labs(x=\\n Survival Time (Days)\,
y=\Survival Probabilities\n\,
title=\Survival Times Of \n Roadside and Vegetated Populations\n\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,
size=12),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=12),
legend.title=element_text(face=\bold\,
size=10))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Plot by all treatments but not including Hemizonia
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
#Filter the data using subset to remove Hemizonia from the dataframe, but it will remain in the metadata so we'll next use droplevels to reconcile the metadata and the dataframe to only have 4 levels
filtered_data<-subset(mydata,Treatment!=\Hemizonia\)
filtered_data$Treatment<-droplevels(filtered_data$Treatment)
levels(filtered_data$Treatment)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIFxcR3Jhc3NsYW5kXFwgICAgICAgICBcXEJpb21hc3MgUmVtb3ZhbFxcICAgXFxSYWtpbmdcXCAgICAgICAgICAgIFxcUmFraW5nICYgQ2xpcHBpbmdcXFxuIn0= -->
[1] Removal & Clipping
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY294X21vZGVsX2dyYXBoMzwtc3VydmZpdChTdXJ2KE51bURheXNBbGl2ZSxDZW5zb3JSZXByb2R1Y3Rpb24pflxuICAgICAgICAgICAgICAgICAgICAgICAgICAgIFRyZWF0bWVudCxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgZGF0YT1maWx0ZXJlZF9kYXRhKVxuXG5zdXJ2aXZhbF9maWx0ZXJlZDwtYXV0b3Bsb3QoY294X21vZGVsX2dyYXBoMykrXG4gIGxhYnMoeD1cXERheXMgdG8gRmxvd2VyaW5nXFwsXG4gICAgICAgeT1cXFByb3BvcnRpb24gQWxpdmVcXCxcbiAgICAgICB0aXRsZT1cXFxcKStcbiAgdGhlbWUocGxvdC50aXRsZT1lbGVtZW50X3RleHQoaGp1c3Q9MC41KSxcbiAgICAgICAgYXhpcy50aXRsZS54PWVsZW1lbnRfdGV4dChmYWNlPVxcYm9sZFxcLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGNvbG9yPVxcQmxhY2tcXCxzaXplPTIwKSxcbiAgICAgICAgYXhpcy50aXRsZS55PWVsZW1lbnRfdGV4dChmYWNlPVxcYm9sZFxcLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGNvbG9yPVxcQmxhY2tcXCxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBzaXplPTIwKSxcbiAgICAgICAgbGVnZW5kLnRpdGxlPWVsZW1lbnRfdGV4dChmYWNlPVxcYm9sZFxcLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIHNpemU9MTIpKVxuICAgICAgICAjc2NhbGVfZmlsbF9kaXNjcmV0ZShuYW1lPVxcVHJlYXRtZW50XFwpXG5zdXJ2aXZhbF9maWx0ZXJlZFxuXG5nZ3NhdmUocGxvdD1zdXJ2aXZhbF9maWx0ZXJlZCxmaWxlPVxcL1VzZXJzL01pcmFuZGEvRG9jdW1lbnRzL0VkdWNhdGlvbi9VQyBTYW50YSBDcnV6L0RpdHRyaWNoaWEvRGl0dHJpY2hpYV9BbmFseXNpcy9maWd1cmVzL3N1cnZpdmFsX2ZpbHRlcmVkLnBuZ1xcLHdpZHRoPTI1LGhlaWdodD0xNSx1bml0cz1cXGNtXFwsZHBpPTgwMClcbmBgYFxuYGBgIn0= -->
```r
```r
cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
Treatment,
data=filtered_data)
survival_filtered<-autoplot(cox_model_graph3)+
labs(x=\Days to Flowering\,
y=\Proportion Alive\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
#scale_fill_discrete(name=\Treatment\)
survival_filtered
ggsave(plot=survival_filtered,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_filtered.png\,width=25,height=15,units=\cm\,dpi=800)
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5jb3hfc2ltcGxlX2Zsb3dlcjwtY294cGgoU3VydihOdW1EYXlzQWxpdmUsQ2Vuc29yUmVwcm9kdWN0aW9uKX5cbiAgICAgICAgICAgICAgICAgICAgICAgIFRyZWF0bWVudCxcbiAgICAgICAgICAgICAgICAgICAgICBkYXRhPWZpbHRlcmVkX2RhdGEpXG5zdW1tYXJ5KGNveF9zaW1wbGVfZmxvd2VyKVxuYGBgXG5gYGAifQ== -->
```r
```r
cox_simple_flower<-coxph(Surv(NumDaysAlive,CensorReproduction)~
Treatment,
data=filtered_data)
summary(cox_simple_flower)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Call: coxph(formula = Surv(NumDaysAlive, CensorReproduction) ~ Treatment, data = filtered_data)
n= 640, number of events= 219
coef exp(coef) se(coef) z Pr(>|z|) TreatmentBiomass Removal 1.5366 4.6489 0.2249 6.832 8.38e-12
TreatmentRaking -0.1909 0.8262 0.2882 -0.663 0.507601
TreatmentRaking & Clipping 0.8405 2.3176 0.2296 3.661 0.000252
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01
‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95TreatmentBiomass Removal 4.6489 0.2151 2.9916 7.224 TreatmentRaking 0.8262 1.2104 0.4696 1.453 TreatmentRaking & Clipping 2.3176 0.4315 1.4777 3.635
Concordance= 0.591 (se = 0.025 ) Likelihood ratio test= 76.49 on 3 df, p=<2e-16 Wald test = 63.39 on 3 df, p=1e-13 Score (logrank) test = 70.96 on 3 df, p=3e-15
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY294X2Z1bGxfZmxvd2VyPC1jb3htZShTdXJ2KE51bURheXNBbGl2ZSxDZW5zb3JSZXByb2R1Y3Rpb24pflxuICAgICAgICAgICAgICAgICAgICAgICAgVHJlYXRtZW50K1xuICAgICAgICAgICAgICAgICAgICAgICAgKDF8QmxvY2spLFxuICAgICAgICAgICAgICAgICAgICAgIGRhdGE9ZmlsdGVyZWRfZGF0YSlcbnN1bW1hcnkoY294X2Z1bGxfZmxvd2VyKVxuYGBgXG5gYGAifQ== -->
```r
```r
cox_full_flower<-coxme(Surv(NumDaysAlive,CensorReproduction)~
Treatment+
(1|Block),
data=filtered_data)
summary(cox_full_flower)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: filtered_data events, n = 219, 640 Iterations= 3 20 NULL Integrated Fitted Log-likelihood -1009.607 -939.5368 -923.4011
Chisq df p AIC BICIntegrated loglik 140.14 4.00 0 132.14 118.58 Penalized loglik 172.41 11.35 0 149.72 111.27
Model: Surv(NumDaysAlive, CensorReproduction) ~ Treatment + (1 | Block) Fixed coefficients coef exp(coef) se(coef) z p TreatmentBiomass Removal 1.7331470 5.6584329 0.2386310 7.26 3.8e-13 TreatmentRaking -0.1003176 0.9045501 0.2991949 -0.34 7.4e-01 TreatmentRaking & Clipping 1.0582399 2.8812952 0.2467012 4.29 1.8e-05
Random effects Group Variable Std Dev Variance Block Intercept 0.8028534 0.6445736
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuYW5vdmEoY294X3NpbXBsZV9mbG93ZXIsY294X2Z1bGxfZmxvd2VyKVxuYGBgXG5gYGAifQ== -->
```r
```r
anova(cox_simple_flower,cox_full_flower)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiQW5hbHlzaXMgb2YgRGV2aWFuY2UgVGFibGVcbiBDb3ggbW9kZWw6IHJlc3BvbnNlIGlzICBTdXJ2KE51bURheXNBbGl2ZSwgQ2Vuc29yUmVwcm9kdWN0aW9uKVxuIE1vZGVsIDE6IH5UcmVhdG1lbnRcbiBNb2RlbCAyOiB+VHJlYXRtZW50ICsgKDEgfCBCbG9jaylcbiAgIGxvZ2xpayAgQ2hpc3EgRGYgUCg+fENoaXwpICAgIFxuMSAtOTcxLjM2ICAgICAgICAgICAgICAgICAgICAgICAgXG4yIC05MzkuNTQgNjMuNjU0ICAxIDEuNDQzZS0xNSAqKipcbi0tLVxuU2lnbmlmLiBjb2RlczogIDAg4oCYKioq4oCZIDAuMDAxIOKAmCoq4oCZIDAuMDEg4oCYKuKAmSAwLjA1IOKAmC7igJkgMC4xIOKAmCDigJkgMVxuIn0= -->
Analysis of Deviance Table Cox model: response is Surv(NumDaysAlive,
CensorReproduction) Model 1: ~Treatment Model 2: ~Treatment + (1 |
Block) loglik Chisq Df P(>|Chi|)
1 -971.36
2 -939.54 63.654 1 1.443e-15 *** — Signif. codes: 0 ‘’ 0.001
‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuQUlDKGNveF9zaW1wbGVfZmxvd2VyLGNveF9mdWxsX2Zsb3dlcikgI3VzZSB0aGUgZnVsbCBtb2RlbCwgaXQgaGFzIHRoZSBsb3dlciBBSUMgc2NvcmVcbmBgYFxuYGBgIn0= -->
```r
```r
AIC(cox_simple_flower,cox_full_flower) #use the full model, it has the lower AIC score
<!-- rnb-source-end -->
<!-- rnb-frame-begin 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 -->
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["df"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["AIC"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"3.00000","2":"1948.728","_rn_":"cox_simple_flower"},{"1":"11.34631","2":"1869.495","_rn_":"cox_full_flower"}],"options":{"columns":{"min":{},"max":[10],"total":[2]},"rows":{"min":[10],"max":[10],"total":[2]},"pages":{}}}
</script>
</div>
<!-- rnb-frame-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#Using Patchwork to put three graphs together
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-plot-begin -->
<img 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" />
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#2022 data
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<!-- rnb-source-begin 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 -->
```r
```r
mydata_dig<-read.csv(\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/borr_dig_trim_control_2022.csv\,stringsAsFactors=T)
mydata_shade<-read.csv(\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/borr_shade_none_2022.csv\,stringsAsFactors=T)
mydata_water<-read.csv(\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/borr_water_nutrients_roots_2022.csv\,stringsAsFactors=T)
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Let's manipulate the dig data!
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```r
```r
pldate_dig<-mydata_dig %>%
select(Plant_ID,Row,Treatment,PlantDate.L.,PlantDate.R.)%>%
pivot_longer(cols=starts_with(\PlantDate\),names_to=\Position\,values_to=\PlantDate\,names_prefix=\PlantDate.\)%>%
mutate(Position=str_replace(Position,\.$\,\\))
plmeas_dig<-mydata_dig%>%
select(Plant_ID,Row,Treatment,LeftMeasure..mm.,RightMeasure..mm.)%>%
rename(\Measure_mm_L\=\LeftMeasure..mm.\,\Measure_mm_R\=\RightMeasure..mm.\) %>%
pivot_longer(cols=starts_with(\Measure\),names_to=\Position\,values_to=\Measurement_mm\,names_prefix=\Measure_mm_\)
plmort_dig<-mydata_dig%>%
select(Plant_ID,Row,Treatment,Mortality.Date..L.,Mortality.Date..R.)%>%
pivot_longer(cols=starts_with(\Mortality\),names_to=\Position\,values_to=\MortalityDate\,names_prefix=\Mortality.Date..\)%>%
mutate(Position=str_replace(Position,\.$\,\\))
dig_work<-left_join(pldate_dig,plmeas_dig,by=c(\Plant_ID\,\Row\,\Treatment\,\Position\))%>%
left_join(.,plmort_dig,by=c(\Plant_ID\,\Row\,\Treatment\,\Position\))
library(lubridate)
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Attaching package: ‘lubridate’
The following objects are masked from ‘package:base’:
date, intersect, setdiff, union
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```r
```r
dig_calc<-dig_work%>%
mutate(Censor=1,NumDaysAlive=(as.numeric(yday(mdy(MortalityDate)))-as.numeric(yday(mdy(PlantDate)))))
cox_model_dig<-survfit(Surv(NumDaysAlive,Censor)~
Treatment,
data=dig_calc)
survival_dig<-autoplot(cox_model_dig)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
#scale_fill_discrete(name=\Treatment\)
survival_dig
ggsave(plot=survival_dig,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_dig.png\,width=25,height=15,units=\cm\,dpi=800)
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" />
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Let's manipulate the shade data!
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```r
```r
pldate_shade<-mydata_shade %>%
select(Plant_ID,Plot,Treatment,PlantDate.L.,PlantDate.R.)%>%
pivot_longer(cols=starts_with(\PlantDate\),names_to=\Position\,values_to=\PlantDate\,names_prefix=\PlantDate.\)%>%
mutate(Position=str_replace(Position,\.$\,\\))
plmeas_shade<-mydata_shade%>%
select(Plant_ID,Plot,Treatment,LeftMeasure.mm.,RightMeasure.mm.)%>%
rename(\Measure_mm_L\=\LeftMeasure.mm.\,\Measure_mm_R\=\RightMeasure.mm.\) %>%
pivot_longer(cols=starts_with(\Measure\),names_to=\Position\,values_to=\Measurement_mm\,names_prefix=\Measure_mm_\)
plmort_shade<-mydata_shade%>%
select(Plant_ID,Plot,Treatment,Mortality.Date..L.,Mortality.Date..R.)%>%
pivot_longer(cols=starts_with(\Mortality\),names_to=\Position\,values_to=\MortalityDate\,names_prefix=\Mortality.Date..\)%>%
mutate(Position=str_replace(Position,\.$\,\\))
shade_work<-left_join(pldate_shade,plmeas_shade,by=c(\Plant_ID\,\Plot\,\Treatment\,\Position\))%>%
left_join(.,plmort_shade,by=c(\Plant_ID\,\Plot\,\Treatment\,\Position\))
library(lubridate)
shade_calc<-shade_work%>%
mutate(Censor=1,NumDaysAlive=(as.numeric(yday(mdy(MortalityDate)))-as.numeric(yday(mdy(PlantDate)))))
cox_model_shade<-survfit(Surv(NumDaysAlive,Censor)~
Treatment,
data=shade_calc)
survival_shade<-autoplot(cox_model_shade)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
survival_shade
ggsave(plot=survival_shade,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_shade.png\,width=25,height=15,units=\cm\,dpi=800)
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" />
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Let's manipulate the water data!
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```r
```r
pldate_water<-mydata_water %>%
select(Plant_ID,Plot,Competition,Treatment,PlantDate.L.,PlantDate.R.)%>%
pivot_longer(cols=starts_with(\PlantDate\),names_to=\Position\,values_to=\PlantDate\,names_prefix=\PlantDate.\)%>%
mutate(Position=str_replace(Position,\.$\,\\))
plmeas_water<-mydata_water%>%
select(Plant_ID,Plot,Competition,Treatment,LeftMeasure.mm.,RightMeasure.mm.)%>%
rename(\Measure_mm_L\=\LeftMeasure.mm.\,\Measure_mm_R\=\RightMeasure.mm.\) %>%
pivot_longer(cols=starts_with(\Measure\),names_to=\Position\,values_to=\Measurement_mm\,names_prefix=\Measure_mm_\)
plmort_water<-mydata_water%>%
select(Plant_ID,Plot,Competition,Treatment,Mortality.Date..L.,Mortality.Date..R.)%>%
pivot_longer(cols=starts_with(\Mortality\),names_to=\Position\,values_to=\MortalityDate\,names_prefix=\Mortality.Date..\)%>%
mutate(Position=str_replace(Position,\.$\,\\))
water_work<-left_join(pldate_water,plmeas_water,by=c(\Plant_ID\,\Plot\,\Competition\,\Treatment\,\Position\))%>%
left_join(.,plmort_water,by=c(\Plant_ID\,\Plot\,\Competition\,\Treatment\,\Position\))
library(lubridate)
water_calc<-water_work%>%
mutate(Censor=1,NumDaysAlive=(as.numeric(yday(mdy(MortalityDate)))-as.numeric(yday(mdy(PlantDate)))))
cox_model_water<-survfit(Surv(NumDaysAlive,Censor)~
Treatment+Competition,
data=water_calc)
survival_water<-autoplot(cox_model_water)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
#scale_fill_discrete(name=\Treatment\)
survival_water
ggsave(plot=survival_water,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_water.png\,width=25,height=15,units=\cm\,dpi=800)
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```r
```r
#Now just looking at each treatment with weed cloth or no weed cloth present
water_calc_w<-water_calc%>%
filter(Treatment==\Water\)
cox_model_Twater<-survfit(Surv(NumDaysAlive,Censor)~
Competition,
data=water_calc_w)
survival_Twater<-autoplot(cox_model_Twater)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
#scale_fill_discrete(name=\Treatment\)
survival_Twater
ggsave(plot=survival_Twater,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Twater.png\,width=25,height=15,units=\cm\,dpi=800)
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<img 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/>
<!-- rnb-plot-end -->
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```r
```r
water_calc_f<-water_calc%>%
filter(Treatment==\Fertilizer\)
cox_model_Tfertilizer<-survfit(Surv(NumDaysAlive,Censor)~
Competition,
data=water_calc_f)
survival_Tfertilizer<-autoplot(cox_model_Tfertilizer)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
#scale_fill_discrete(name=\Treatment\)
survival_Tfertilizer
ggsave(plot=survival_Tfertilizer,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Tfertilizer.png\,width=25,height=15,units=\cm\,dpi=800)
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
water_calc_c<-water_calc%>%
filter(Treatment==\Control\)
cox_model_Tcontrol<-survfit(Surv(NumDaysAlive,Censor)~
Competition,
data=water_calc_c)
survival_Tcontrol<-autoplot(cox_model_Tcontrol)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
#scale_fill_discrete(name=\Treatment\)
survival_Tcontrol
ggsave(plot=survival_Tcontrol,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Tcontrol.png\,width=25,height=15,units=\cm\,dpi=800)
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img src="data:image/png;base64,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/>
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```r
```r
#Now just looking at each weed cloth or no weed cloth present for all treatments
water_calc_wt<-water_calc%>%
filter(Competition==\Weed Cloth\)
cox_model_Weedt<-survfit(Surv(NumDaysAlive,Censor)~
Treatment,
data=water_calc_wt)
survival_Weedt<-autoplot(cox_model_Weedt)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
survival_Weedt
ggsave(plot=survival_Weedt,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Weedt.png\,width=25,height=15,units=\cm\,dpi=800)
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<img 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/>
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
water_calc_Nt<-water_calc%>%
filter(Competition==\No Cloth\)
cox_model_Nonet<-survfit(Surv(NumDaysAlive,Censor)~
Treatment,
data=water_calc_Nt)
survival_Nonet<-autoplot(cox_model_Nonet)+
labs(x=\Time (Days)\,
y=\Survival\,
title=\\)+
theme(plot.title=element_text(hjust=0.5),
axis.title.x=element_text(face=\bold\,
color=\Black\,size=20),
axis.title.y=element_text(face=\bold\,
color=\Black\,
size=20),
legend.title=element_text(face=\bold\,
size=12))
survival_Nonet
ggsave(plot=survival_Nonet,file=\/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Nonet.png\,width=25,height=15,units=\cm\,dpi=800)
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5saWJyYXJ5KHBhdGNod29yaylcbmBgYFxuYGBgIn0= -->
```r
```r
library(patchwork)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG5BdHRhY2hpbmcgcGFja2FnZTog4oCYcGF0Y2h3b3Jr4oCZXG5cblRoZSBmb2xsb3dpbmcgb2JqZWN0IGlzIG1hc2tlZCBmcm9tIOKAmHBhY2thZ2U6TUFTU+KAmTpcblxuICAgIGFyZWFcbiJ9 -->
Attaching package: ‘patchwork’
The following object is masked from ‘package:MASS’:
area
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGlicmFyeShnZ3Bsb3QyKVxuY29tYmluZWRfcGxvdDwtc3Vydml2YWxfV2VlZHQrc3Vydml2YWxfTm9uZXRcbmBgYFxuYGBgclxuY29tYmluZWRfcGxvdFxuYGBgXG5gYGAifQ== -->
```r
```r
library(ggplot2)
combined_plot<-survival_Weedt+survival_Nonetcombined_plot
<!-- rnb-source-end -->
<!-- rnb-frame-begin eyJtZXRhZGF0YSI6eyJjbGFzc2VzIjoiZGF0YS5mcmFtZSIsIm5yb3ciOjIsIm5jb2wiOjQsInN1bW1hcnkiOnsiRGVzY3JpcHRpb24iOiJkZiBbMiDDlyA0XSJ9fSwicmRmIjoiSDRzSUFBQUFBQUFBQTRWUVVRckNNQXpOdWxXeG9BaGV3QXRzb0lLSTMrSUJSTUhmdWswZHpuWnNGVDI5bW00THpQNzRFZnJ5MHJ3a2I3YzVMc1JSQUlBUEFXUGdjNFRBRC90dHVBSmtNUEVnZ0lGOVgvaHBnaURBR0dJMHhTYTZPZnRmL3hIbVN0N1RDc0c0Rm0rbkdRSTVnUk9CMGhFWWxQb1pkVVZZVy9CbjZ5bkJPVUpuYnB6TGlscUlGSWswTWpxWHFJYloyMm5wNjhKa1dtRVRzMDV3cDlsdTlrT01IOHJ1bFlUeDlhRnU0WEptSnppMmRDM3pHbnZ0VFBacHRYaXIxVXZWSlZNcExaL0xVMHJXak5DQSt2Nm9LRE5Gemdsa3E4aG9JK21maUhWT1RIMGN2TDlZbnNRQ0FRSUFBQT09In0= -->
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
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</script>
</div>
<!-- rnb-frame-end -->
<!-- rnb-plot-begin -->
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" />
<!-- rnb-plot-end -->
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```r
```r
#Try this from Julia
#WH_PA + WH_subset + choice + plot_layout(guides = 'collect') +
# plot_annotation(tag_levels = 'A', tag_suffix = ')') &
# theme(plot.tag.position = c(0, 1),
# plot.tag = element_text(size = 11.5, hjust = 0, vjust = 0))
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<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##Histograms
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuI051bWJlciBvZiBEYXlzIEFsaXZlIC0gQnkgVHJlYXRtZW50XG5nZ3Bsb3Qoc2hhZGVfY2FsYyxhZXMoeD1OdW1EYXlzQWxpdmUpKStnZW9tX2hpc3RvZ3JhbSgpK2ZhY2V0X3dyYXAodmFycyhUcmVhdG1lbnQpKSAjSGVyZSB3ZSBzZWUgdGhhdCB0aGUgMyB0cmVhdG1lbnRzIGFyZSByaWdodCBza2V3ZWQuXG5gYGBcbmBgYCJ9 -->
```r
```r
#Number of Days Alive - By Treatment
ggplot(shade_calc,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that the 3 treatments are right skewed.
<!-- rnb-source-end -->
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<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5nZ3Bsb3QoZGlnX2NhbGMsYWVzKHg9TnVtRGF5c0FsaXZlKSkrZ2VvbV9oaXN0b2dyYW0oKStmYWNldF93cmFwKHZhcnMoVHJlYXRtZW50KSkgI0hlcmUgd2Ugc2VlIHRoYXQgdGhlIDMgdHJlYXRtZW50cyBhcmUgcmlnaHQgc2tld2VkLlxuYGBgXG5gYGAifQ== -->
```r
```r
ggplot(dig_calc,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that the 3 treatments are right skewed.
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5nZ3Bsb3Qod2F0ZXJfY2FsYyxhZXMoeD1OdW1EYXlzQWxpdmUpKStnZW9tX2hpc3RvZ3JhbSgpK2ZhY2V0X3dyYXAodmFycyhUcmVhdG1lbnQpKSAjSGVyZSB3ZSBzZWUgdGhhdCB0aGUgMyB0cmVhdG1lbnRzIGFyZSByaWdodCBza2V3ZWQuXG5gYGBcbmBgYCJ9 -->
```r
```r
ggplot(water_calc,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that the 3 treatments are right skewed.
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
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ffdV6pjo9Q7+HJbo5SOciCAAAIIIIAAAgggUKcAAW+dgKyOAAIIIIAAAggg0NgCBLyN3T6UDgEEEEAAAQQQQKBOAQLeOgFZHQEEEEAAAQQQQKCxBXL3pTX9zJ++EZn2oCcKlP+qStJ56tdX9EtjeuZt2g/LyKI+8lFHfrnpW6xpDt7THtLOR19GUF5qo7QH5ZX2tq26aFsIekRcef30KJ60B9qxNuFq2rG2HF5eq4jHDh1vszjmSrHSeUQ/wBPny4ovt0htY7Rj7W5as1I71pb6y2spHtBf2ue0Ih87vPPJy6pdx3IX8OpRWWlveCJqa2szPWc2zUHBrp7BuXTp0tTrlEV9ZKXHgqxbt879PGqadnpmsILDoB+0SDJftY/aKeonl5PITzuqTnrt7e1JJBeahn6yWW0UZ9vWI3nSHorYjnpmdZbtmPYJMqtjh54asXr16tSPHWoffeBL+0Os3BTAaF+LeiKGypLFvkY71nY0k5uGOMfM2nL471r6QNK3b9/UtwUdc/WnfS3tIYttTudO7V/60axKsSFdGtJucdJHAAEEEEAAAQQQ6FEBAt4e5SdzBBBAAAEEEEAAgbQFCHjTFiZ9BBBAAAEEEEAAgR4VIODtUX4yRwABBBBAAAEEEEhbgIA3bWHSRwABBBBAAAEEEOhRAQLeHuUncwQQQAABBBBAAIG0BQh40xYmfQQQQAABBBBAAIEeFSDg7VF+MkcAAQQQQAABBBBIW4CAN21h0kcAAQQQQAABBBDoUQEC3h7lJ3MEEEAAAQQQQACBtAUIeNMWJn0EEEAAAQQQQACBHhUg4O1RfjJHAAEEEEAAAQQQSFuAgDdtYdJHAAEEEEAAAQQQ6FEBAt4e5SdzBBBAAAEEEEAAgbQFmtPOgPQRaCSBadOmhRZn8uTJofOYgQACCCCAQKMJcE6L3yIEvPGtWLIAAvPmzQutBQFvKA0zEEAAAQQaUIBzWvxGoUtDfCuWRAABBBBAAAEEEMihAAFvDhuNIiOAAAIIIIAAAgjEFyDgjW/FkggggAACCCCAAAI5FCDgzWGjUWQEEEAAAQQQQACB+AIEvPGtWBIBBBBAAAEEEEAghwIEvDlsNIqMAAIIIIAAAgggEF+AgDe+FUsigAACCCCAAAII5FCAgDeHjUaREUAAAQQQQAABBOILEPDGt2JJBBBAAAEEEEAAgRwKEPDmsNEoMgIIIIAAAggggEB8gcwD3jvuuMPWrl3bpYTz58+3W265xZYtW9ZlOm8QQAABBBBAAAEEEKhXINOA984777T//d//tRUrVpTKPWXKFLvwwgvtwQcftOOPP94WLVpUmscIAggggAACCCCAAAL1CjTXm0Dc9XX19uc//7kNHTq0tMrChQtt7ty5NmPGDOvVq5dde+21Nn36dDvjjDNKyzCCAAIIIIAAAggggEA9ApkEvB0dHXb++efbKaecYhdccEGpvAsWLLAxY8a4YFcTx44da7Nnzy7N18hDDz1k9913X2naYYcdZm1tbaX3aY307t3bBgwYkFbyLl3loaFfv362fft2N57Wvyzqo7I3NTVZS0tL6nbKQ9tVc3Nym3BQeyt9fRgLmpdkW8lNeaWdj+fVv3//isVPuywqQBrtGFSxLNsxi+3f347aD9Icsjp2qA59+vRxx5A066M8tE/rNc1BbtqvK+1HWRxfVE/asbbWjtuOtaX+8lraDpI+dgRte8pDQ9C8l0uTzFgW25z2MQ2tra22ZcuWyIInFy1EZKMruLvuuqsdcMABXZZasmSJDRkypDRt8ODBtnz58tJ7jSjYveyyy0rT3v72t9tuu+1Wep/myKBBg9JMvpR2FhueMsuqPtrw9Je3IconaN4Pf/jD0Cp+9atfDZ0XNcM7GEUtk8S8gQMHVkwmqM4VV2rwBbKqUyO1YxJNUotb2P4RtW/07dvX9Fc+1JJWeRr+92kHu/68KtkpKIjzAdSfZq3jlcpSa7rl64W1Y/ly9b5vpHasty7e+knWKaq9o+Z5ZUniNat8dOGw0pB6wPvss8/aX/7yF7viiiu6lUU7+rZt20rTt27d6q52liZ0jkyePNn9edOWLl1qL774ovc2tVddRV65cmVq6SthnRR32GEH92U91T3NIYv6qPwjR460devWWXt7e5rVcScIbTubNm1KLJ+g7UofyNROQV+o/Otf/xqa96c//enQeUEz9OleB4bVq1cHzU5smj6IDBs2zF566SXXVlEJB3lELV/LPJ3ok27HoHJEtWPQ8rVOUzvqw8SaNWtqTSLWev52TPvuUK3HjrD9I2zfGDVqlHNbv359N4Nq0+qWgG+C2kfHjUpXg3yr1DQqN1190ndWVLewQeXIYl+rtR3Dyh02Paodw9apZXqW7ajypR0P6K6NPigkee4M2q50zNXxMGheLe0QtU4W25yOuYo7Vq1a5e76Rn14TP1LazpQPffcc3bEEUeYuiMIedKkSXbvvffaiBEjunyBrdKBIQqWeQgggAACCCCAAAIIBAmkfoVXwa3+vOGoo44y3Z4aPXq0+zR/8cUX2+LFi90n4FmzZtm4ceO8RXlFAAEEEEAAAQQQQKBugdQD3qgSqs/uCSec4AJi3WZVEDxx4sSoVZiHAAIIIIAAAggggEBVApkHvNddd12XAk6YMMHGjx/v+lTF+SJNl5V5gwACCCCAAAIIIIBABYHMA96g8uhLQVl9qzkof6YhgAACCCCAAAIIFFcg9S+tFZeOmiGAAAIIIIAAAgjkQYCANw+tRBkRQAABBBBAAAEEahYg4K2ZjhURQAABBBBAAAEE8iBAwJuHVqKMCCCAAAIIIIAAAjULEPDWTMeKCCCAAAIIIIAAAnkQIODNQytRRgQQQAABBBBAAIGaBQh4a6ZjRQQQQAABBBBAAIE8CBDw5qGVKCMCCCCAAAIIIIBAzQIEvDXTsSICCCCAAAIIIIBAHgQIePPQSpQRAQQQQAABBBBAoGYBAt6a6VgRAQQQQAABBBBAIA8CBLx5aCXKiAACCCCAAAIIIFCzAAFvzXSsiAACCCCAAAIIIJAHAQLePLQSZUQAAQQQQAABBBCoWYCAt2Y6VkQAAQQQQAABBBDIgwABbx5aiTIigAACCCCAAAII1CzQXPOarIgAAggggAACCCBQGIFp06YF1mXy5MmB0/M0kYA3T61FWRFAAAEEEEAAgZQE5s2bF5hyEQJeujQENi0TEUAAAQQQQAABBIoiQMBblJakHggggAACCCCAAAKBAgS8gSxMRAABBBBAAAEEECiKAAFvUVqSeiCAAAIIIIAAAggEChDwBrIwEQEEEEAAAQQQQKAoAgS8RWlJ6oEAAggggAACCCAQKEDAG8jCRAQQQAABBBBAAIGiCBDwFqUlqQcCCCCAAAIIIIBAoAABbyALExFAAAEEEEAAAQSKIkDAW5SWpB4IIIAAAggggAACgQIEvIEsTEQAAQQQQAABBBAoigABb1FaknoggAACCCCAAAIIBAoQ8AayMBEBBBBAAAEEEECgKALNeaxIU1NTJsVOOx8vfb1642lVLIs8/GVPuz7KK+k6RZU5ap6/3t54rctXu56XX9xXL33vNWq9OMtErR9nnpeH9xpnnXqWSTsfpe/91VPOSut69cgqLy+/SuWKM79SWpXm+/OoZtny9Wpd159OnPE4+cRZJk5eUcsojyzy8cqQdl5e+t6rl28ar8oj7Xy8PJLMJyqtqHlBhtUurzS0Ti3rBeUfNq2a9HMX8Pbr18969+4dVvfEpre0tNiQIUMSSy8ooV69/nuBfeDAgdbR0RG0SGLTmpubU6+PCquNr2/fvqb80hy0DchMeSU1BLW3tgO1U9C8qHyrXV5pZbnNDRo0KKr4bl4tdaiYaNkCXjtqv05zqLUdqy2Ttv8s9jXv2BGnHautQ/nySdcnarvSdtCnT5/yIoS+j0orbCXVR9tD2sdc5aGhUhm1D1RaJqwu1UxPuh2j8q62HaPSCpun+uivUdoxrJxxp+vYoW1Bf0kNQduVl37QvKh8q11eaWW5zfXv3982b94cVQVLNyqJzLq2mRs2bLCtW7fWtnIVa7W1tdmqVauqWKP6RXVAbG1ttbVr16ZepyzqI4GRI0faxo0brb29vXqQKtbQxr1t2zbbtGlTFWtFLxrU3trJ1U5B86JSq3Z5BTAKXlavXh2VbN3ztL3pb82aNVYpyKy2DrUULo12DCpHre0YlFbUNLWjPsDKN83B347bt29PMytL+tgRtl1pe9Txff369bHrE5ZWVAJqHx03tmzZErVY3fPkpiBGZYza13QcS/t4qcok3Y5hQLW0Y1haUdOzbEeVo5ZtLar85fMUHOoCTpLbQlCZdczVh8qgeeVl8r+vdnmtm8U2p2OutjkdNyp9+KEPr79FGUcAAQQQQAABBBAonAABb+GalAohgAACCCCAAAII+AUIeP0ajCOAAAIIIIAAAggUToCAt3BNSoUQQAABBBBAAAEE/AIEvH4NxhFAAAEEEEAAAQQKJ0DAW7gmpUIIIIAAAggggAACfgECXr8G4wgggAACCCCAAAKFEyDgLVyTUiEEEEAAAQQQQAABvwABr1+DcQQQQAABBBBAAIHCCRDwFq5JqRACCCCAAAIIIICAX4CA16/BOAIIIIAAAggggEDhBAh4C9ekVAgBBBBAAAEEEEDAL0DA69dgHAEEEEAAAQQQQKBwAgS8hWtSKoQAAggggAACCCDgFyDg9WswjgACCCCAAAIIIFA4AQLewjUpFUIAAQQQQAABBBDwCxDw+jUYRwABBBBAAAEEECicAAFv4ZqUCiGAAAIIIIAAAgj4BQh4/RqMI4AAAggggAACCBROgIC3cE1KhRBAAAEEEEAAAQT8AgS8fg3GEUAAAQQQQAABBAonQMBbuCalQggggAACCCCAAAJ+AQJevwbjCCCAAAIIIIAAAoUTIOAtXJNSIQQQQAABBBBAAAG/AAGvX4NxBBBAAAEEEEAAgcIJEPAWrkmpEAIIIIAAAggggIBfgIDXr8E4AggggAACCCCAQOEECHgL16RUCAEEEEAAAQQQQMAvQMDr12AcAQQQQAABBBBAoHACBLyFa1IqhAACCCCAAAIIIOAXIOD1azCOAAIIIIAAAgggUDgBAt7CNSkVQgABBBBAAAEEEPALEPD6NRhHAAEEEEAAAQQQKJxAZgHvSy+9ZDfddJM9++yz3RDnz59vt9xyiy1btqzbPCYggAACCCCAAAIIIFCPQCYB74033mhf+9rX7Pnnn7dvfvObNnv27FKZp0yZYhdeeKE9+OCDdvzxx9uiRYtK8xhBAAEEEEAAAQQQQKBegeZ6E6i0fkdHh7t6e84559irX/1qe8Mb3mCXXnqpfeADH7CFCxfa3LlzbcaMGdarVy+79tprbfr06XbGGWdUSpb5CCCAAAIIIIAAAgjEEkg94G1qarJLLrnEFWbLli129913u8BXExYsWGBjxoxxwa7ejx07tsvVX01Td4fHH39co2444IADbNCgQd7b1F4VgPfr1y+19JVw7969Xfp9+/a1bdu2pZpXFvXxKtDc3Jy6XUtLi/NTvZIagtpbbaRtOGheVL7VLq88lFe160WVIWie2kaDtrlKQ9plUf5ptGNQvWptx6C0oqapHbPa/lUOtaMuKqQ5JH3siNqutD1EzS+vZzXLeut6+4D36k1P+tU7vlcqY9K+YfXIKh/lX207hpU5arrXft5r1LL1zIvbjvXkoXXVPqpLpe2lmnyC0lLbaAiaF5V2tcsrrSy2OR1zNfTp08e2bt3qxsP+pR7wehmvWbPGPvWpT9mGDRts2rRpbvKSJUtsyJAh3iI2ePBgW758eem9Rm6//XabOnVqadrMmTNt1113Lb1Pc0SAWQxZBPCqR1b10Y5Ry86RhXVUHkOHDg2dHTUvaKVql/fSaG1t9UZTffXvd2EZ1VqHsPQaYXpWdWqkdkzCPcljR1Qb9O/f3/QXd4hKK24aaS9XqYwKqHTuy2JIsh2jylttO0al1SjzKrVjUuVM8twZVeaoeUF1qXZ5L42strkBAwbY+vXrvWwDXzMLeLVD/+lPf7I777zTTjzxRLvhhhvcFS3/lU1F5+WNPWnSJDvmmGNKhW9vb7cXX3yx9D6tETXuqlWr0krepatPWsOHD3df1qv0yaTegmRRH5Vxxx13tHXr1rm/essctb62E207mzdvjlqsqnlB25W2W7VT+QexSgkHpRW1jj4JDxw40PTBMM1BgVhbW5vpS6Rqq6ih2jpEpRU2L412DMqr1nYMSitqmtpRB961a9dGLVb3PH87bt++ve70ohJI+tgRtl3ttNNOtnr1andRJKo8/nlhafmXKR9X+2zatKni1aDy9ap9LzddfVq5cqWpbmGD7nzWUo+w9MKmJ92OYfnU0o5haUVN74l2jCpPvfP0wUd3bHT+TGoI2q50zNUFj6B5UflWu7zSymKb0zFX5zLtZ7rbFfWBOfWAVwHJAw88YG95y1vczn/IIYfY5Zdfbo899piNGDHCHnnkkZLxihUrbNSoUaX3GlGw4V2C13tF8GkHh8pHQ9q3Cr309eqN/zfndP5nkYdX8qzySjKfqLSi5nl19r/Wuny16/nzjDPupe+9Rq0TZ5mo9auZl1Veaefjpe+9VmNQzbJe+nr1xqtZv9plk8yjUlqV5vvLXs2y/vU0Xs+65WlFvY+TT5xlovKIOy+rfFSerPLKIh/lkUU+SbtFlTlqXtD2VO3yXhq1ruetX+m1mvST6wAZUioFq1deeaXde++9bgn1ydXVstGjR9uBBx5ojz76qC1evNgFsbNmzbJx48aFpMRkBBBAAAEEEEAAAQSqF0j9Cq9u6Zx66qku6L3qqqtMt+L0xAZd3dVwwgknmLotDBs2zAXBEydOrL4WrIEAAggggAACCCCAQIhA6gGv8n3jG9/oAl71v1U/Rf8wYcIEGz9+vOtTVT7PvxzjCCCAAAIIIIAAAgjUIpBJwOsVLCygLe+n6y3PKwIIIIAAAggggAAC9Qqk3oe33gKyPgIIIIAAAggggAAC9QgQ8Najx7oIIIAAAggggAACDS9AwNvwTUQBEUAAAQQQQAABBOoRIOCtR491EUAAAQQQQAABBBpegIC34ZuIAiKAAAIIIIAAAgjUI0DAW48e6yKAAAIIIIAAAgg0vAABb8M3EQVEAAEEEEAAAQQQqEeAgLcePdZFAAEEEEAAAQQQaHgBAt6GbyIKiAACCCCAAAIIIFCPAAFvPXqsiwACCCCAAAIIINDwAgS8Dd9EFBABBBBAAAEEEECgHgEC3nr0WBcBBBBAAAEEEECg4QUIeBu+iSggAggggAACCCCAQD0CBLz16LEuAggggAACCCCAQMMLEPA2fBNRQAQQQAABBBBAAIF6BAh469FjXQQQQAABBBBAAIGGFyDgbfgmooAIIIAAAggggAAC9QgQ8Najx7oIIIAAAggggAACDS9AwNvwTUQBEUAAAQQQQAABBOoRIOCtR491EUAAAQQQQAABBBpegIC34ZuIAiKAAAIIIIAAAgjUI0DAW48e6yKAAAIIIIAAAgg0vAABb8M3EQVEAAEEEEAAAQQQqEeAgLcePdZFAAEEEEAAAQQQaHgBAt6GbyIKiAACCCCAAAIIIFCPAAFvPXqsiwACCCCAAAIIINDwAgS8Dd9EFBABBBBAAAEEEECgHgEC3nr0WBcBBBBAAAEEEECg4QUIeBu+iSggAggggAACCCCAQD0CBLz16LEuAggggAACCCCAQMMLEPA2fBNRQAQQQAABBBBAAIF6BAh469FjXQQQQAABBBBAAIGGF2hu+BKWFbB///7Wu3fvsqnJv21pabG2trbkE/al2NTU5N4NHjzYOjo6fHOSH82iPiq16tS3b19TfmkO2gZkpu0hqSGovZubm61Xr15VbwtBaVUqp/KqZb1K6frnqy4ahgwZ4p8cOJ52WZRpGu0YVJla2zEorUrTGq0dK5W30vykjx1R25X259bW1kpFKs2PSqu0UNmItrk+ffpkcsxV1pXKmMX2onIk3Y5KM2yoth3D0omaLrdGaseossaZp3Onts0kz51B257y0BA0L6qc1S6vtLLc5gYMGGCbN2+OqoLlLuBdv369bd26NbJSScxU465cuTKJpELT0Magg/uaNWtSr1MW9VFFR44caRs3brT29vbQeicxQwfUbdu22aZNm5JIzqUR1N4KDNVOQfOiMq52eQWigwYNstWrV0clW/c8bW/Dhg1z+eiDSdRQbR2i0gqbl0Y7BuVVazsGpRU1Te04cOBAt09HLVfvPH87bt++vd7kItdP+tgRtl2NGjXKdHzXX9whLK2o9dU+Om5s2bIlarG658lNQYzKqLqFDTqfrV27Nmx2YtOTbsewgtXSjmFpRU3Psh1Vjlq2tajyl89TAK9jcpLnzqAy65ir42HQvPIy+d9Xu7zWzWKb0zFXbuvWrav4IZYuDf4WZRwBBBBAAAEEEECgcAIEvIVrUiqEAAIIIIAAAggg4Bcg4PVrMI4AAggggAACCCBQOAEC3sI1KRVCAAEEEEAAAQQQ8AsQ8Po1GEcAAQQQQAABBBAonAABb+GalAohgAACCCCAAAII+AUIeP0ajCOAAAIIIIAAAggUToCAt3BNSoUQQAABBBBAAAEE/AIEvH4NxhFAAAEEEEAAAQQKJ0DAW7gmpUIIIIAAAggggAACfgECXr8G4wgggAACCCCAAAKFEyDgLVyTUiEEEEAAAQQQQAABv0DsgPe9732vtbe3+9d141dffbVNnDix23QmIIAAAggggAACCCDQCALNUYX4xz/+Ybfeeqtb5K677rLzzz/f+vbtW1pl27Zt9sc//tH23HPP0jRGEEAAAQQQQAABBBBoJIHIgHePPfaw008/3TZt2mRbtmyx2bNnW+/evUvlb2lpsVe/+tV25plnlqYxggACCCCAAAIIIIBAIwlEBrzDhw+3u+++25X3gx/8oF1zzTU2cODARio/ZUEAAQQQQAABBBBAIFIgMuD1r/mnP/3JXeldtGiRe/XPUxA8atQo/yTGEUAAAQQQQAABBBBoCIHYAe/1119vkyZNslWrVnUr+FFHHWW///3vu01nAgIIIIAAAggggAACPS0QO+D9/Oc/bx/+8Ift5JNPtmHDhnUpN90cunDwBgEEEEAAAQQQQKCBBGIFvGvWrLGXXnrJLrjgAttxxx0bqPgUBQEEEEAAAQQQQACBaIFYz+EdPHiw7b777nb//fdHp8ZcBBBAAAEEEEAAAQQaTCDWFV6V+bvf/a6ddtpp9vzzz7vgt7n55VV11XffffdtsKpRHAQQQAABBBBAAAEEzF6OWitoqA/v6tWr7cQTT+y2JF9a60bCBAQQQAABBBBAAIEGEYgd8P7nP/+xjo6OwGL7f4wicAEmIoAAAggggAACCCDQQwKxA97W1tYeKiLZIoAAAggggAACCCBQu0DsgHfJkiWhV3j79etnbW1ttZeCNRFAAAEEEEAAAQQQSEkgdsC79957uz68QeWgD2+QCtMQQAABBBBAAAEEGkEgdsB711132bZt20plXrt2rT3wwAN2+eWXu+fzlmYwggACCCCAAAIIIIBAAwnEDnj322+/bsV+xzveYevXr7fvfe97Nm3atG7zmYAAAggggAACCCCAQE8LxA54wwo6atQo++Mf/xg2m+kIIIAAAgg0jMBll13m7lZu3769W5kmT57cbRoTEECgGAKxA957773Xtm7dWqq1xl988UU777zz7NBDDy1NZwQBBBBAAIFGFfjb3/4WWjQC3lAaZiCQe4HYAe9hhx0W+KU1dWs499xzcw9BBRBAAAEEEEAAAQSKKRA74F20aFG3x5INGDDA/D8xXEwiaoUAAggggAACCCCQZ4HYAe/gwYNt1apVds0119hTTz1lO+20k73pTW+yd73rXdanT5+KBitXrrR//vOftu+++5r6/fqH+fPn23PPPWdjx461HXbYwT+LcQQQQAABBBBAAAEE6hKIHfA+8sgjpm4N+onhMWPG2NKlS00/RnH44YfbH/7wB+vbt29oQW644QabMWOGHXzwwe51r732slNPPdUtP2XKFHvsscdszz33dI84u/TSS2233XYLTYsZCCCAAAIIIIAAAghUI9Ar7sInnHCCvfWtb7XFixfbww8/bC+88IJ7Du+TTz5pF198cWgyenbvr3/9a/vOd75j+kLARRddZLfddpvpiu/ChQtt7ty5dtVVV9nXv/51O/roo2369OmhaTEDAQQQQAABBBBAAIFqBWJd4dWzdu+//37TVd5XvepVLo+mpibXpeH000+3G2+80QWsQZn37t3bfvnLX5r6+2rYuHGjtbe3u8fCLFiwwF0t7tXrv3G3ujTMnj27SzIKsBUYe8NrXvOayKvJ3nL1vqpMcbpq1JOP1/+5paXFPIN60otaN4v6ePmrzbOwU506Ojq8bOt+DSqz8tC2HjQvKsNql1c+WbSRt83FKV+cZaIM4sxTeZJux6B8a23HoLSipimfrLZ/lUNtFPR4ragyVjsv6e0yaruq1i4qrWrrqeWTTE9ucdKs5fjiEq7yX9LtGJV9te0YlVbYPOWhc6f80hzitmO9ZVB9knYL2p6Vh4ageVF1qHZ5pZXFNue1v84l/h9HC6pLrIBXhVZgocC3fFi3bl2Xx5WVz9d7L9jVgVlXg9UNQn111SViyJAhpVXUT3j58uWl9xpRMD116tTStJkzZ7p+wKUJKY4MHz48xdRfTnro0KEvv0lxLKv69O/f3/SXtyHKJ2peUD2rXd5LI6prkLdMEq9tbW0Vk6m1DhUT7sEFsqpTI7VjEtxJukWlNXDgQNNf3CEqrbhp+JdLOj2lXSlNnaj950F/eZIer1SWpPKrth2TyjfNdLKyS/LcGVXmqHlBjtUu76VR63re+nFfBw0aFBij+tePFfDq4H1o57N21e3g/PPPtwMOOMA2bdpkd9xxh11yySX2pS99yZ9m4LiWV7cGBc5nnXWWW0afNPwRuZ7t269fvy7rT5w40d7//veXpulTxksvvVR6n9aIDkCrV69OK3mXrj6dKvBYsWJFxQ8N9RYki/qojPogow9GQR+O6q2Df31tJ9p2Nm/e7J9c13jQdqWdSCckdcGpZghKK2p9fajUB0P9ZHeag/YffcBatmxZxS+IVluHWsqdRjsGlaPWdgxKK2qa2lEnLN3FSnPwt2PaV3iTPnaEbVc77rij2/43bNgQmy4srdgJlC2YZHpeEKvziOoWNmzZsqUw5zTVUXVds2aNu5sbVuckpms/0/Hf//sASaRbnoa/HcvnJfle8VBra2ui586g7VnxnC4uBs2Lqk+1yyutpI8dQeXTFd4RI0a4eE3HwqgPDLECXmVy5ZVX2kc+8hEbN26cS1wHdB2YjjjiCPvKV74SVI7SNAU/CpZ32WUX+9rXvuYu22umCqluEt6gwK/8CQ4C8zY4Lacvy6W9gSsfBeb+YFzTkh50ctSgfNLOK4v6eD5Z5KUNW39JugWlpbp4beTVL85rUFpR6ymfrNzi1qfaOkTVL2xeGu0YlFet7RiUVtS0nmhHGaY5JL1dRm1X1e7TUWnVYpJkenLTyThOmnGWqaU+/nWSbkd/2uXjWeSlPKrdXsrLGee98tGQdhtpW0naLajMtdYnKK1KfknXJyg/L47StuDVLWg5TYsd8OopCnqs2K233mp6jJg+JehpC+95z3vC0i5NP/vss+31r3+9nXLKKaVpGjnwwANdFwf101WgO2vWLBdQd1mINwgggAACCCCAAAII1CEQO+BVf9szzzzTDjnkEHdFV3139QWyb3zjG/bFL34x9AconnjiCbvnnnvc3+9///tSUfV75nq8mZ7+MGnSJBs2bJiNHj3a1IWBAQEEEEAAAQQQQACBpARiB7ynnXaaewbvGWec4fJW3zt9mUxBsALVI488MrBMe++9t3v0WODMzokTJkyw8ePHuz7B1XxZISw9piOAAAIIIIAAAggg4BeIFfCqY/jNN9/sfiBi5513duur34Sem6sv2ejX18ICXn9mYeP68pb+GBBAAAEEEEAAAQQQSFrgv9+aqpCqOlPrm6T6sYnyQd/G1JfNGBBAAAEEEEAAAQQQaESBWAGvrr6++93vdv119QUzb9CPUeingfVcXQYEEEAAAQQQQAABBBpRIFbAq4JfffXV7rl6u+22m3tMmPrw6ikLhx56qOnX1hgQQAABBBBAAAEEEGhEgVh9eFVwPUVh7ty57pFkDz30kPvJuP3339/22WefRqwXZUIAAQQQQAABBBBAwAnEDni1tL6opqcu6I8BAQQQQAABBBBAAIE8CMTu0pCHylBGBBBAAAEEEEAAAQTKBQh4y0V4jwACCCCAAAIIIFAoAQLeQjUnlUEAAQQQQAABBBAoFyDgLRfhPQIIIIAAAggggEChBAh4C9WcVAYBBBBAAAEEEECgXICAt1yE9wgggAACCCCAAAKFEiDgLVRzUhkEEEAAAQQQQACBcgEC3nIR3iOAAAIIIIAAAggUSoCAt1DNSWUQQAABBBBAAAEEygUIeMtFeI8AAggggAACCCBQKAEC3kI1J5VBAAEEEEAAAQQQKBcg4C0X4T0CCCCAAAIIIIBAoQQIeAvVnFQGAQQQQAABBBBAoFyAgLdchPcIIIAAAggggAAChRIg4C1Uc1IZBBBAAAEEEEAAgXIBAt5yEd4jgAACCCCAAAIIFEqAgLdQzUllEEAAAQQQQAABBMoFCHjLRXiPAAIIIIAAAgggUCgBAt5CNSeVQQABBBBAAAEEECgXaC6fwHsEEEAAAQQQQAABBOoRmDp1qm3evLlbEpMnT+42LYsJBLxZKJMHAggggAACCCDwChKYM2dOYG17KuClS0NgczARAQQQQAABBBBAoCgCBLxFaUnqgQACCCCAAAIIIBAoQMAbyMJEBBBAAAEEEEAAgaIIEPAWpSWpBwIIIIAAAggggECgAAFvIAsTEUAAAQQQQAABBIoiQMBblJakHggggAACCCCAAAKBArl7LFn//v2td+/egZVJcmJLS4u1tbUlmWS3tJqamty0wYMHW0dHR7f5SU7Ioj4qr+rUt29fU35pDtoGZKbtIakhqL2bm5utV69eVW8LQWlVKqfyqmW9Sun656suGoYMGeKfHDiedlmUaRrtGFSZWtsxKK1K0xqtHSuVt9L8pI8dUduV9ufW1tZKRSrNj0qrtFAVI0mm5x0DK6WZxfYigqTbMYq12naMSitsntz69OmTyblTZajUjmHljDtd504dD73tJu56UcsFldmLn4LmVZtW1PJR86rNOyotb96AAQMCn/nrzddr7gLe9evX29atW/11SGVcDbJy5cpU0vYS1Yatg/uaNWtSr1MW9VG9Ro4caRs3brT29navmqm86oC6bds227RpU2LpB7W3AkO1U9C8qIyrXV6B6KBBg2z16tVRydY9T9vbsGHDXD76YBI1VFuHqLTC5qXRjkF51dqOQWlFTVM7Dhw40O3TUcvVO8/fjtu3b683ucj1kz52hG1Xo0aNMh3f9Rd3CEsr7vrlyyWZntwUxChN1S1s0Pls7dq1YbMTm550O4YVrJZ2DEsrarr2Mx3/t2zZErVY3fPkpiHJbSOoUArgdUxO8twZVGYdc3U8DJoXVC5vWrXLe+sFvSaZlo65clu3bl3FDz90aQhqDaYhgAACCCCAAAIIFEaAgLcwTUlFEEAAAQQQQAABBIIECHiDVJiGAAIIIIAAAgggUBgBAt7CNCUVQQABBBBAAAEEEAgSIOANUmEaAggggAACCCCAQGEECHgL05RUBAEEEEAAAQQQQCBIgIA3SIVpCCCAAAIIIIAAAoURIOAtTFNSEQQQQAABBBBAAIEgAQLeIBWmIYAAAggggAACCBRGgIC3ME1JRRBAAAEEEEAAAQSCBAh4g1SYhgACCCCAAAIIIFAYAQLewjQlFUEAAQQQQAABBBAIEiDgDVJhGgIIIIAAAggggEBhBAh4C9OUVAQBBBBAAAEEEEAgSKA5aCLTEEAge4FLLrnENm/e3C3jyZMnd5vGBAQQQAABBBCIL0DAG9+KJRFIVWDOnDmB6RPwBrIwEQEEEEAAgdgCdGmITcWCCCCAAAIIIIAAAnkUIODNY6tRZgQQQAABBBBAAIHYAgS8salYEAEEEEAAAQQQQCCPAgS8eWw1yowAAggggAACCCAQW4CANzYVCyKAAAIIIIAAAgjkUYCAN4+tRpkRQAABBBBAAAEEYgsQ8MamYkEEEEAAAQQQQACBPAoQ8Oax1SgzAggggAACCCCAQGwBAt7YVCyIAAIIIIAAAgggkEcBAt48thplRgABBBBAAAEEEIgtQMAbm4oFEUAAAQQQQAABBPIoQMCbx1ajzAgggAACCCCAAAKxBQh4Y1OxIAIIIIAAAggggEAeBQh489hqlBkBBBBAAAEEEEAgtgABb2wqFkQAAQQQQAABBBDIowABbx5bjTIjgAACCCCAAAIIxBYg4I1NxYIIIIAAAggggAACeRTINOBtb2+3u+++u5vT/Pnz7ZZbbrFly5Z1m8cEBBBAAAEEEEAAAQTqEcgs4N24caN9+9vfthtuuKFLeadMmWIXXnihPfjgg3b88cfbokWLusznDQIIIIAAAggggAAC9QhkEvAuWLDAjjvuOFu7dm2Xsi5cuNDmzp1rV111lX3961+3o48+2qZPn95lGd4ggAACCCCAAAIIIFCPQHM9K8ddd/369XbWWWfZ8uXL7c9//nNpNQXCY8aMsV69/ht3jx071mbPnl2arxF1c1i6dGlp2pAhQ6ylpaX0Pq2RpqYma25Ol6d3796u+N5rWnVRulnUxyu/2jNtO2+bSTKfoLRqzScoLc8n6NXLJ2hetWkFpeFN87a1OGnGWcZLt9ZXr95p55VlPsor7fr423H79u218sdaL+ljR5RNtXZRacWqXNlCSaYntzh2cZYpK2ZNb7PKR4Wrth1rqZDy0H7Q0dFRy+qx14nbjrETDFlQdUnaLWh7Vh4aguaFFK2m5bNKS+2jQfWqdCxMN6L7/zXeb7/93NicOXP+/5T/vixZssQUwHrD4MGDXVDsvdfrddddZ1OnTi1Nmjlzpu27776l92mOjBgxIs3kS2kPGzasNJ7mSFb1GTBggOkvb0OUT9S8oHpWu3xQGt60JNPy0hw+fLg3GvqaRr6hmWU0I6s69evXL5MaxWnHJAqSpFtUWoMGDTL9xR2i0oqbhn+5pNNT2pXSVODhPw/6y5P0eKWyJJVfte1Ya75ZnmeyskuyTlFljpoX1B7VLh+UhjctybS8NLUP6eJq1JBJwBtWAH2i2bZtW2n21q1brfxE8ZGPfMTGjRtXWqatrS2TL7dphy3vglEqREIjOtANHTrUVq5c2cUhoeS7JJNFfZShgvcNGza4vy4FSPhN37593ae5zZs3J5Zy0JcmBw4c6D4Jr1q1qqp8gtKKSsD71B20TLVpBaXhTevTp4/pg+WKFStcW3nTg16TzDcofU1Lox2D8qq1HYPSipqmdtQxbN26dVGL1T3P346VrmrUm1nSx46w7WqHHXYwfbFZ3/eIO4SlFXf98uWSTE/7mYY1a9aY6hY26LyXZL5h+STdjmH51NKOYWlFTe/fv7/p+C+/NAd/O6aZj+Kh1tbWikFbNWUI2q50zNXxMGheVNrVLp9VWrrCqw/+2s8UT2q7CBt6NOBVlP/II4+UyqaT8KhRo0rvNbLTTju5P2+iujds2bLFe5vaq26TZJGPKqAdNu2dNsv66ASctp26tWjjTjKfoLRUl1rsgtKK2lijAt5q04qTT5w04ywTlVeceWm0Y1C+tbZjUFpR09SOOmmlbedtL8on7YC3lu0/yijKptp9OiqtqDKEzUsyPbWLTsaV0kzaN6xuWeWj/Kttx7AyR01XHjpvVvKNSiPOPG//SjsftY8ugiWZT1BaXpfQoHlRHtUun1Va3rFQ24MMo4ZMvrQWVoADDzzQHn30UVu8eLHbcGfNmtXlam7YekxHAAEEEEAAAQQQQCCuQI9e4dWtghNOOMEmTZrkbq+OHj3aJk6cGLfsLIcAAggggAACCCCAQEWBTAPeQw891PTnHyZMmGDjx4+3TZs2uX4l/nmMI4AAAggggAACCCBQr0CmAW9YYdWnxOtXErYM0xFAAAEEEEAAAQQQqEWgR/vw1lJg1kEAAQQQQAABBBBAoBqBhrjCW02BWRYBBBBAAAEEEMirwLRp07oVXU8b0KPJjuv8VVqGdAQIeNNxJVUEEEAAAQQQQKCbwLx587pN8yYQ8HoSyb/SpSF5U1JEAAEEEEAAAQQQaCABAt4GagyKggACCCCAAAIIIJC8AAFv8qakiAACCCCAAAIIINBAAgS8DdQYFAUBBBBAAAEEEEAgeQEC3uRNSREBBBBAAAEEEECggQQIeBuoMSgKAggggAACCCCAQPICBLzJm5IiAggggAACCCCAQAMJEPA2UGNQFAQQQAABBBBAAIHkBQh4kzclRQQQQAABBBBAAIEGEiDgbaDGoCgIIIAAAggggAACyQsQ8CZvSooIIIAAAggggAACDSRAwNtAjUFREEAAAQQQQAABBJIXIOBN3pQUEUAAAQQQQAABBBpIgIC3gRqDoiCAAAIIIIAAAggkL0DAm7wpKSKAAAIIIIAAAgg0kAABbwM1BkVBAAEEEEAAAQQQSF6AgDd5U1JEAAEEEEAAAQQQaCABAt4GagyKggACCCCAAAIIIJC8AAFv8qakiAACCCCAAAIIINBAAgS8DdQYFAUBBBBAAAEEEEAgeQEC3uRNSREBBBBAAAEEEECggQSaG6gsFAWBQIFp06Z1m97c3GwnnXRSt+lMQAABBBBAAAEEygUIeMtFeN9wAvPmzQssEwFvIAsTEUAAAQQQQKBMgC4NZSC8RQABBBBAAAEEECiWAAFvsdqT2iCAAAIIIIAAAgiUCRDwloHwFgEEEEAAAQQQQKBYAgS8xWpPaoMAAggggAACCCBQJkDAWwbCWwQQQAABBBBAAIFiCRDwFqs9qQ0CCCCAAAIIIIBAmUDuHkvWv39/6927d1k1kn/b0tJibW1tySfsS7Gpqcm9Gzx4sHV0dPjmJD+aRX1UatWpb9++pvzSHgYOHGjaHpIagtpbz/vt1atX1dtCUFq1ljPJtFQXDUOGDKlYnCTzDctM+7K2/STbMSgvfztOnTo1aBE37dRTTw2dF3eG8krbrpp2jFvusOWSPnZE2Wg7aG1tDStKt+lRaXVbOMaEJNPzjoGV0sxie1HVk27HKM5q2zEqrbB5cuvTp08m506VoVI7hpWz2ulJ5hOUlhc/Bc2LKmu1y2eVlpfPgAEDbPPmzd7bwNfcBbzr16+3rVu3BlYmyYlq3JUrVyaZZLe0dADSwX3NmjWp1ymL+qiCI0eOtI0bN1p7e3u3+iY9QXls2rQpsWSD2luBodopaF5UxtUu7wUwQWlWm1ZQGt40bW/Dhg2z1atXuw8m3vSg1yTzDUpf03Ri3LZtW6LtGJSXvx3nzJkTtIibduyxx4bOizND7agPYtqn0xz87bh9+/Y0s3In+iS3hbC0Ro0aZTq+6y/uEJZW3PXLl0syPR1zdQFAaapuYYPOZ2vXrg2bndj0rM4BtbRjLZXUfqbj/5YtW2pZPfY6ctOQ5LYRlXmS+QSlpWOujodB83qyXFF5R83TMVcX2datW1fxww9dGqIkmYcAAggggAACCCCQewEC3tw3IRVAAAEEEEAAAQQQiBIg4I3SYR4CCCCAAAIIIIBA7gUIeHPfhFQAAQQQQAABBBBAIEqAgDdKh3kIIIAAAggggAACuRcg4M19E1IBBBBAAAEEEEAAgSiB3D2WLKoyeZ83bdq0wCpMnjw5cDoTEUAAAQQQQAABBCoLEPBWNspsiXnz5gXmRcAbyMJEBBBAAAEEEEAglgBdGmIxsRACCCCAAAIIIIBAXgUIePPacpQbAQQQQAABBBBAIJYAAW8sJhZCAAEEEEAAAQQQyKsAAW9eW45yI4AAAggggAACCMQSIOCNxcRCCCCAAAIIIIAAAnkVIODNa8tRbgQQQAABBBBAAIFYAoV9LBnPtI3V/iyEAAIIlASCjpt9+vSxY489trQMIwgggEBaAkHHIOWVxONZCxvw8kzbtDZH0kUAgaIKhB03CXiL2uLUC4HGEgg7BiUR8NKlobHamtIggAACCCCAAAIIJCxAwJswKMkhgAACCCCAAAIINJYAAW9jtQelQQABBBBAAAEEEEhYgIA3YVCSQwABBBBAAAEEEGgsAQLexmoPSoMAAggggAACCCCQsAABb8KgJIcAAggggAACCCDQWAKFfSxZYzFnX5ryZ9npWZqbN29O5Fl22deGHBFAAIFXtkD5Md3TSOJxTV5aSb/mscxJG5Be4wgQ8DZOWyRakjSfZZdoQUkMAQQQQKCiQB6P6XlcBTP+AAAboklEQVQsc8WGYIHcCtClIbdNR8ERQAABBBBAAAEE4ggQ8MZRYhkEEEAAAQQQQACB3AoQ8Oa26Sg4AggggAACCCCAQBwBAt44SiyDAAIIIIAAAgggkFsBAt7cNh0FRwABBBBAAAEEEIgjwFMaYijxaJUYSAVe5KKLLrJNmzYVuIb5rVrYvqkaNfLjmvIrnm7JL7/8ctu6dWvsTGj/2FSxFwwzZX+KTciCDSpAwBujYXi0SgykAi9yxx13FLh2+a5a2L6pWnGCzl/b3nnnnVUVmvaviivWwmGm7E+x+FiogQXo0tDAjUPREEAAAQQQQAABBOoXIOCt35AUEEAAAQQQQAABBBpYoCEC3vnz59stt9xiy5Yta2AqioYAAggggAACCCCQR4EeD3inTJliF154oT344IN2/PHH26JFi/LoSJkRQAABBBBAAAEEGlSgR7+0tnDhQps7d67NmDHDevXqZddee61Nnz7dzjjjjAblolgIIIAAAggggAACeRPo0YB3wYIFNmbMGBfsCm7s2LE2e/bsLobr1q0z/XnD9u3bS8t706p5VWAdd6i0bKX5lfLx1terNx60TtS8oOWjpiWZVlg+TU1NkfUJW6/a6UnnE2SjPGoZgtKKSidq+ah5UWkGzfPS8l6DlvGmxVnGW7bWV/km3Y5eWfzl99rRP81bzv9aab5/2aBxrZ9Wffz5eeX0Xv3z0hhPMp8k04qqay351LJOWBnibnNav558q1m3mmXL61XNupX2gWrSKi+H9155VMrHW7ae12rasZ58vHWTsIlKq9b6pF0ur8xhr2H5e9NVr46OjrDV3fQeDXiXLFliQ4YMKRVw8ODBtnz58tJ7jfzqV7+yqVOnlqbNnDnT9t1339L7sJGvfvWrgbNGjhwZOD1oYt++fd3kJNIKSt+bNnz48MTzSbvMXtmDXgcOHGj6S2oIq0tbW1vVWYSlpYTCto2odcIKEJZW2PKaHpZPLWlF5aN5I0aMqLRIqEfFFTNcIMxMRQhy07Rq16mlOv37969ltarXidOO1SQaZhNkWSndWtIKW6dSXkHzw8oclUfYOkHpx51WKc2WlhYbOnRoxeTCyl0pfX/C3jnNP618PIl8dC7XXxJplZfP/z7J84w/3aDxapyD1vdPC3PRMtXmU2taYfmEpRe2vL9e5eO1pFXLOspX+9D69evLi9DlfVNnRBwdEndZPNk36sLw/PPPl3YKjX/5y1+266+/vpTRM888Y08//XTpvYLdAQMGlN6nNaI8/FeW08ind+/e7qCwZs0a27ZtWxpZlNLMoj7KTB9g9CMNGzduLOWdxkifPn1MV/ureUh9LeVQ4KJ2Wrt2bS2rx15Hn0779etXcYeNnWDIgjq56iSxatWqiifZlStXhqSS3OQitqOCig0bNiSHFJCSvx3TPoRndezQB1idsNL+kRe1z5YtWzI55mq/bm9vt6gP56tXr3bHsoBmTnRSlu2oc+fmzZsTLX95Ylm1oxdUqx3THHSlUsfDLM6d2hayOL5nsc1pH1Owq/ZRPBD14bFHr/Dq6sQjjzxS2oZWrFhho0aNKr3XyGtf+1r3501cunRp6huE8lLwkfaGp5OWBh3g0w7csqiP6qKAV3VJ204HB31ISPvk2Nra6m43ZlGfLA52XnAUxy3tOmt7KWI7ar9O287fjvrgl+aQ1bFDdVAgmrZdc3OzC8aUV5qD3HQyrlQftWWlZZIoZ5btmMU5IMt2lH/abaT66HiYdj7KI4v6KI8stjmvPtqfveOiq2DAv/gdWgNWrnfSgQceaI8++qgtXrzYBUmzZs2ycePG1Zss6yOAAAIIIIAAAgggUBLo0Su86uNzwgkn2KRJk2zYsGE2evRomzhxYqlwjCCAAAIIIIAAAgggUK9Ajwa8KvyECRNs/Pjx7ta011em3kqxPgIIIIAAAggggAACnkCPB7wqiPq8ef1ZvYLxigACCCCAAAIIIIBAEgI92oc3iQqQBgIIIIAAAggggAACUQIEvFE6zEMAAQQQQAABBBDIvQABb+6bkAoggAACCCCAAAIIRAkQ8EbpMA8BBBBAAAEEEEAg9wIEvLlvQiqAAAIIIIAAAgggECVAwBulwzwEEEAAAQQQQACB3AsQ8Oa+CakAAggggAACCCCAQJQAAW+UDvMQQAABBBBAAAEEci9AwJv7JqQCCCCAAAIIIIAAAlECBLxROsxDAAEEEEAAAQQQyL9AB0OPCfzzn//s2GuvvTqeeOKJHitD0hkfdNBBHZdddlnSyfZYemeeeWbHRz/60R7LP+mMb7vtNrfNvfjii0kn3dDpnXHGGYVqx1tvvbVQ7bh9+3ZXn9/+9rcNvR1VU7iTTz65Y9KkSdWskvtlt23b5trx2muvzX1dvAqcdNJJHZMnT/be5v71d7/7nWujzZs3574uqsCyZctcfW666aaK9eEKbw9+ZulsHes80JteizKoPvoryqC6dB7Ei1Idt60VbZuL0zhF2y69Y0ecuudhGa8+RTp26LhRpPrE2Y5oxzhKPbuM10Z6LcJQTX0IeIvQ4tQBAQQQQAABBBBAIFSgOXQOM1IXaGtrs8MPP9wGDRqUel5ZZfCe97zH9txzz6yySz2f/fff34YPH556PlllMHLkSLfN9e3bN6ssGyIfteMOO+zQEGVJohBeO7a2tiaRXI+n0dTU5LbL3XbbrcfLklQB3vzmN7/irvAWtR2LcjVU2/auu+7q9rVevYpxvVPHQMVRO+20U8Vdt6mzIYtxXbtiVVkAAQQQQAABBBBA4JUoUIwQ/5XYctQZAQQQQAABBBBAIJZA7293DrGWZKG6BbZs2WL33XefLV++3HRLUrd/vKHzm4Y2d+5c9wWpvN16Xbt2rc2bN8923313rzq2adMm+8c//mHPP/+8u9XQu3fv0rxGHvnPf/5jd999t/tyV3lXhvnz59sDDzxgQ4YMsf79+zdyNUpl27p1q2sHtdGIESNK0zWS522uS0UC3qxcudK1Y79+/bp1GcpjO3pVVNl1/PAfI/Lajo899pg9+OCDrsuQ2skb8nrsWL16tdvmBg8e3O34kOdtzmuXsNenn37aHn74YXecb2lpKS2W13b0KnDHHXe4/czfbSiP7ah2uOeee2zx4sU2atQo85+L81gftc+CBQvsX//6l9vmmptf7plb6VhIwOtt3Sm/dj4Gyj73uc+5IFcH+p/85Cc2YcIEU2PpoH/qqafa0KFD3XT1r9x7771TLlFyyZ9//vmmg8NHPvIRl+iGDRvss5/9rK1bt86dADRv/PjxXQL85HJPLiW1Q+djyNxB7g9/+IPpYPDWt77VZTBlyhT74x//aJ2PcrHLL7/c3v72t7vAN7nck09p/fr19ulPf9rUV0sftFT+973vfS6jvG9zUVo33HCD/ehHP3Lt0/mYK9MJ+S1veYtbJY/t6NV11apV9oUvfMEGDhxob3zjG3PdjldeeaWpnQYMGGAXXXSR7bHHHrbzzjtbXo8d+pD8v//7v+4Y/rOf/cz13fWO4Xne5rxtL+z19NNPdxcB9DSKqVOn2ujRo22XXXbJbTt69bzzzjvtW9/6ljte6rysIY/tqHNA5+PxTK+6+KRz1wc/+EEXd+SxPmqH73//+y7e0FNQfvjDH9o73/lOd1Ej1jlNfXgZ0he45JJLOjoPhKWMOnemjlmzZrn3xxxzTMdDDz3kxvV81M5AuKPzU1lp2UYeufnmmzs6g9sO1cEbfvGLX3R07kze244TTjih4+9//3vpfaOO+Mu5cePGDrWR2uHZZ5/t6AzmO/SMSQ16Vuj3vve9Rq1GqVx/+tOfOjpPwqX3n/zkJzuefPJJ9z7P21ypQgEjnVe03fN2O68AuLmdH7o6PvCBD3SsWLEit+3oVVPPEj7uuOM6tH95Qx7b8YUXXuj42Mc+1tF518FVQ88j//Wvf+3G83rs6Lxg0TFnzhxXh6eeeqrj4x//uBvP67HDFb7Cv84rbB2dH6hLS91+++0dp512mnuf13ZU4ZcuXdpx7LHHuvPwwoULXX3y2o7l56qf//znHZ1X43N7LOy8c9fRefGsQ+dnDZ0XDjuuuOIKNx7nWEgfXn1kyGA48cQT7TOf+UwpJ93+0tUM3XLWJ68xY8a4eerqoNvlnSeF0rKNOqLb/9dcc419/vOf71JEXVEbO3ZsaZrGH3/88dL7RhzR1WiV+/Wvf73dcsst9n//93/2ne98x/r06eNun6h9vG+15qE+Mtbtq3//+9+mdlLddIVQTwbJ8zZXadvR7bpf/vKXpe41nQdGa29vd12FdBssj+2oOnd+OHZtd+CBB5YI8tqOnQGu6akZnYGFdT4s3j3VRXciNOTx2KFy6+r0vffe666kqSvXjjvuqMm5PXa4wlf4t88++9hVV11VWkrnNO1vGvLajp2Rk+mO5SmnnGL+J9nk9dihbVJ3I9WlQXchOgN5dwzMa310d0vdZu6//37T9vboo4+6fS3usZCAt7S7pjuiwMnr39T5SdgFubq9/NJLL7nbev7+vOoj2nlFKt0C1Zm6bmGdd955riuGbkv6B3XfUD82b9C4+h028qB2UD/Cr3/966YuJ7o9qVs+GpYsWdKl+0Ie6qNy67FIOikdffTRrotJ5ydgd3DI6zanOsUZvO1R2+jFF1/sHlmjPq95bUd9IJ45c6Y7Cfvrn9d2VKDbeeXMBRba13QS1glZQx6PHSp35y+qWeddLDviiCPs6quvtq985SuanNttzhW+wj9dAPD6Xmtb7LxKbzrGaMhrO86YMcM9tuuAAw7oUvu8Hju0r6lb15///Ge78cYbXRc3XWjLa33UBfR//ud/7Bvf+IYdeeSRpu9FqYtG3GPhy719uzQvb9IS6LzNbL/5zW9cvzV9WtGVxfJf8tKnFf+ny7TKUk+62ok6fxbZ9SXs/GnkLknpKpu/TqqPd2DssmADvVFwpE+M5557rr3pTW9yV2o6f1LYncjyWB/Rqi+hvqym/si6yqlg/lWvepW97nWv69I+WjYP25zKGXfQFzV0hV5XbM466yy3Wh7bUe2iD5Zf/epXu+1D5fVRJfPQjtrX9IH++uuvd1+g0VV3baPqZ11epzwcO+SuK4ITJ060D3/4wy7w1V0vr355OxaqPtUMnbf73bFF39vw+srnsR1Vj7/85S/WeYu8W/XzWB9VQtuenouvvtYa9B2Vv/3tb7ndz3TH8gc/+IF1dplxd1XUJ1l9eju7I8Y6p3GF120G2fzTJ+Df//73dumll7rO/cpVTwJQ0KsTtDfoZKBbZI086BOjvgR12GGH2Ze+9CXTweJDH/qQK7KupvmvUOehPt4tSF0R1aBuJXqqwTPPPONey+uj7gKNPujWsU7AumOgL5K8973vdbdd87rNxfXWFzQUIOoHXfQBRndXNKg989aOuhKqD5SdfSPdvnbdddfZr371K/dljby2o9pBXYcURGjQBzCvy1Mejx36Zrj+9KVdXYHSl2h0PFG75XGbc40S85+3berLlPoStjfksR3/+te/2nPPPeeu0uu8pqvUunKvbgF5bUdth945TW3j7Wt5rU/nd53cF8n1JVedoz/xiU+4pxDFPRYS8Hp7aMqvChC1Q+nTo/rpeoMOkAcddJDpyq8GfTtU/Sz118jD9OnTXV9X9Xft/EKe6zOpb11r0AFfn5TVn0snAj2yTFdNG3lQcKRvvt96662umArg1Y9XBwj1m1RfIT3WRVec1J9y3LhxjVwdVzb13dJj1DSo3Or3pCswed3mXEVi/Dv77LNdQKXbXl5QpdXy2I46sM+ZM6e0rx111FHutrEC+ry2o558okBJfcs16LioPr0a8njsUHCnOyd6qosGBUp6LN6+++6by23OVSLGPx3bdXtZ+9shhxzSZY08tqOCW22LOqfpT7/c9dOf/tQd6/N47FCDHHzwwXbbbbe5p4boCUN33XWX68Ob1/ro/KVjh7plaFB/+WrOafzSmmNL/1/nt5LdAd7fV1e3zL/85S+7T5U6cOjkrH5R6j+qQCsvgzZAdfTXl4U0KLg655xzXJCo+nQ+HcB0om70YdGiRc5egYQO5uqHpwO3BvV/0pX5YcOGuavz3/3ud13A0ch1UhcN3fJRH1DdRdBBTrda1Sa6kpHnbS7MXduibm9p8O9rl112mTvQ57Ed/XX98Y9/7K5sHHfccW5yXttRX1abNm2ae8Sa9jfdptRVmrweO3TlSV3V1HVI3Wh0zHvXu97l2ijv25x/+/OPa1tU1zb/fqbjo+785bUd/fXTOUuPvdKj1jTksR3Vx/WCCy5wd1B0F1kXQbzuDXmsj9rh2muvdRcGFcDrCrYeu6afS45zLCTglWCDDPoWvffMvwYpUl3FUP9R9d3VCS1Pg67OqB38B3KVXwcPHTTU9zpPg660qw2C2qFo21ycdslrO0bVLY/tqP6FChDV5aZ8yOuxQx8svS9N+utUxG3OX7+w8by2Y1h98tqO6ualCx3l3w3Ka330oVJXeYN+ACrqWEjAG7ZlMx0BBBBAAAEEEECgEAL04S1EM1IJBBBAAAEEEEAAgTABAt4wGaYjgAACCCCAAAIIFEKAgLcQzUglEEAAAQQQQAABBMIECHjDZJiOAAIIIIAAAgggUAiBfH19vhDkVAIBBBpJQM/c1MPl9Wzb8m8x65nLerzbpz/96VSKrGdY66kg3qAnFuiZrvq5TO8HM7x5Wbzed9997mdIVd/Xvva1XbLUD+foMUDjx4+3f/3rX+5Z23q0HQMCCCCQBwEC3jy0EmVEAIHUBBTw/uhHP3KB7YUXXtglH/2Yip5jnFbAO3XqVPdQeAWXetSOfoxBv66m55nqVxn1Iw1ZDt/+9rfdMy712/R6hrN/UMCrH1PwAl49o5SA1y/EOAIINLIAXRoauXUoGwIIZCLQ0tJiF110kf3973/PJD9/Jp/61KfcLzzdfvvt9thjj7kHqOunqxVk6zm1WQ36ZcGbb77Z/Ryz9yMKYXlPnDjRFBQzIIAAAnkRIODNS0tRTgQQSE1g7Nix7srlZz/72dLPVpZnNnfu3G5XNPWrUvolIw16iPvkyZPt3//+t1vu/e9/v+kX+fQDC7Nnz7aPf/zj7pcVH3nkkfKku7zXT9Xqp8YXLFjgftrUm6n3p556qh1++OF25JFHmq5G69eGNOgnUKdMmeIt6l71y4H61bk1a9aYHsZ+1lln2WGHHeZ+f17L64qyf9AvJepKs36NT7/6eM011/hndxnXT3qqLBr0K3b61TT/8Pjjj9uJJ57ofl5c0/VLZLJR/lpPwTUDAgggkKUAAW+W2uSFAAINK6Cg7cUXX7RvfetbgWV88sknuwWB999/vwtOtYICWwWSuuWvgPGggw5yP5mrAFU/F/62t73Nnn76afvwhz8cmL5/4k477WSvfvWrXV9ZTX/22WfdTyOvWLHCPvOZz9iee+7prsR+85vfdKvp1//OPPNMF9x66SiAVaA5ePBgd7X4jjvuMF2Z1U9Mf+1rX3M/B+4tq9ef//znpqvN6jv8iU98wq644gr/7C7jqoe6OGhQv+czzjjDBfzeQldddZUL/DVPV67VNUNXq/VzrQqWx4wZQ9DrYfGKAAKZCBDwZsJMJggg0OgCu+yyi7tKqiul8+bNq7m4Cha///3v29lnn20TJkyw2267zdQXWFc2FVQqeNWXvioNuur81FNPucXmz5/vgtCrr77aBaW6qvyxj33M7rnnHjdfQbSCy+uvv76UrALS4447zr1XfY499lj3Xl/O07x99tmntOydd95pzzzzjB1zzDFumq50K1j20i8tGDCi+upnPm+66SY3d+vWrfbb3/7WlIYG5fe+973PTdNVXnUb2W233ex73/uem88/BBBAIAsBvrSWhTJ5IIBALgQUpM2YMcMFawr4ahkOOOCA0mrqIrDffvu5Jy9oororaNCV5P3339+Nh/1TVwQ9tUGDrhIfeuih7mrpE088YeoyoEBaV4I1KNg9+uijTX1vVQcFlerS8MlPftLNV7CrrgoKdNXV4kMf+pD7Apqb2flPgfjuu+9uDz/8sPvTdH1x7sorr7S3vOUt3mKBr4MGDXLB9/Tp0+2II45w/YA3btxoH/3oR23Tpk0uPfVJ/sY3vlFaX1fAdXWcAQEEEMhKgCu8WUmTDwII5EJAt+P1hSz1eS0fyvu9qt9u+aBA0T8oIPSGpqYmbzTyVfnoqq73aDD1+1VAevzxx5e6BBxyyCFd0lCgO2fOHHvhhRdcYKvg0yuLrlrrKvPrX/9606PQFIR7AejatWvtuuuuc2mpO4f3p64Qv/vd70zdKCoNylv9jpWWgmr1V+7fv7/rYrF9+3ZTl4tevXqV/t773ve6gLhSusxHAAEEkhLgCm9SkqSDAAKFEFDXBj0u7HOf+5y7Crrzzju7erW2tnZ7aoK6J6Qx6Ert4sWLS49D0+PC9tprL3dVV1dHNdx1112u37CXv/rm7r333u4K9cyZM+1nP/uZm7V+/XoXuKp7hf4UgJ577rl23nnnmdJVUKtpDzzwQOmKslbUI9L0TGB1ozj99NO9bAJfFXzrKq66Mijw1dVnDSNGjHB9iGXo78KgR8HpyRgMCCCAQFYCXOHNSpp8EEAgNwLqAqB+p/6+tq973evcFUt9GUxXdhXY3XjjjXXXSYGtngChfrTqg6svgJ100kmuz62CWA3quqArreorq6u/ulqrrhfqMuAfdKX1/PPPN11J1pfnNPTr189+/OMfuyu6elqD0li6dKkpsFdXCAXGuhrsdZ/w0hs5cqR94AMfsJ/85CfdnujgLeO9Kr/jOvsL64tzu+66q/uCnjfv5JNPdld95aUv9qme6lKxbNkybxFeEUAAgdQFCHhTJyYDBBDIo4C6NgwdOrRUdPVl/eIXv+i6FSiIPOecc0rdAkoL1TCiLgAHH3yw6SqpuizoWbi6+updoVWSusKqgFRBqPoBX3rppe6xZPqimfr6eoOe3atAUq/Nzf+9gadgVI8O07IKctXNQVdgFVyrP7C+mKYnPwQNusqtL87pSQuVBn1I0K/GKej2D/rynvoS60t26t6h5fSUCD2xgQEBBBDISqCp82pB14cxZpUz+SCAAAI5FFAXAfVVVfCZ9bB8+XL32DB/v2B/GdT3WFdY9eUzdYEoH/RoMF3hVVeDrAddFfe6SWSdN/khgAACBLxsAwgggEDOBXSVV88J1pVfPQHi1ltvzXmNKD4CCCCQrABfWkvWk9QQQACBzAXUjUFdLvbYYw/TF8IYEEAAAQS6CnCFt6sH7xBAAIFcCujZt/oSGgMCCCCAQHcBAt7uJkxBAAEEEEAAAQQQKJAAT2koUGNSFQQQQAABBBBAAIHuAgS83U2YggACCCCAAAIIIFAgAQLeAjUmVUEAAQQQQAABBBDoLkDA292EKQgggAACCCCAAAIFEiDgLVBjUhUEEEAAAQQQQACB7gL/DxsN/SrC1HDCAAAAAElFTkSuQmCC" />
<!-- rnb-plot-end -->
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<!-- rnb-text-begin -->
##Cox Model
Start by making a simple model with no random effects. This will be compared to the full model with random effects.
<!-- rnb-text-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY294X3NpbXBfc2hhZGU8LWNveHBoKFN1cnYoTnVtRGF5c0FsaXZlLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICBDZW5zb3IpflxuICAgICAgICAgICAgICAgICAgICAgICAgICBUcmVhdG1lbnQsXG4gICAgICAgICAgICAgICAgICAgICAgICBkYXRhPXNoYWRlX2NhbGMpXG5zdW1tYXJ5KGNveF9zaW1wX3NoYWRlKVxuYGBgXG5gYGAifQ== -->
```r
```r
cox_simp_shade<-coxph(Surv(NumDaysAlive,
Censor)~
Treatment,
data=shade_calc)
summary(cox_simp_shade)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Call: coxph(formula = Surv(NumDaysAlive, Censor) ~ Treatment, data = shade_calc)
n= 88, number of events= 88
coef exp(coef) se(coef) z Pr(>|z|) TreatmentShade 1.0497 2.8569 0.2448 4.287 1.81e-05 ***
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95TreatmentShade 2.857 0.35 1.768 4.616
Concordance= 0.631 (se = 0.032 ) Likelihood ratio test= 18.83 on 1 df, p=1e-05 Wald test = 18.38 on 1 df, p=2e-05 Score (logrank) test = 19.85 on 1 df, p=8e-06
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQoY294X3NpbXBfc2hhZGUpXG5gYGBcbmBgYCJ9 -->
```r
```r
print(cox_simp_shade)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiQ2FsbDpcbmNveHBoKGZvcm11bGEgPSBTdXJ2KE51bURheXNBbGl2ZSwgQ2Vuc29yKSB+IFRyZWF0bWVudCwgZGF0YSA9IHNoYWRlX2NhbGMpXG5cbiAgICAgICAgICAgICAgICAgY29lZiBleHAoY29lZikgc2UoY29lZikgICAgIHogICAgICAgIHBcblRyZWF0bWVudFNoYWRlIDEuMDQ5NyAgICAyLjg1NjkgICAwLjI0NDggNC4yODcgMS44MWUtMDVcblxuTGlrZWxpaG9vZCByYXRpbyB0ZXN0PTE4LjgzICBvbiAxIGRmLCBwPTEuNDI2ZS0wNVxubj0gODgsIG51bWJlciBvZiBldmVudHM9IDg4IFxuIn0= -->
Call: coxph(formula = Surv(NumDaysAlive, Censor) ~ Treatment, data = shade_calc)
coef exp(coef) se(coef) z pTreatmentShade 1.0497 2.8569 0.2448 4.287 1.81e-05
Likelihood ratio test=18.83 on 1 df, p=1.426e-05 n= 88, number of events= 88
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJlZGljdChjb3hfc2ltcF9zaGFkZSlcbmBgYFxuYGBgIn0= -->
```r
```r
predict(cox_simp_shade)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
[1] 1.049723 1.049723 1.049723 1.049723 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.049723 [14] 1.049723 1.049723 1.049723 0.000000 0.000000 1.049723 1.049723 1.049723 1.049723 0.000000 0.000000 1.049723 1.049723 [27] 0.000000 0.000000 1.049723 1.049723 0.000000 0.000000 0.000000 0.000000 1.049723 1.049723 1.049723 1.049723 0.000000 [40] 0.000000 1.049723 1.049723 0.000000 0.000000 0.000000 0.000000 1.049723 1.049723 1.049723 1.049723 1.049723 1.049723 [53] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.049723 1.049723 1.049723 1.049723 0.000000 0.000000 0.000000 [66] 0.000000 1.049723 1.049723 0.000000 0.000000 1.049723 1.049723 1.049723 1.049723 0.000000 0.000000 0.000000 0.000000 [79] 1.049723 1.049723 1.049723 1.049723 1.049723 1.049723 0.000000 0.000000 0.000000 0.000000
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGlzdChwcmVkaWN0KGNveF9zaW1wX3NoYWRlKSlcbmBgYFxuYGBgIn0= -->
```r
```r
hist(predict(cox_simp_shade))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5jb3hfc2ltcF9kaWc8LWNveHBoKFN1cnYoTnVtRGF5c0FsaXZlLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICBDZW5zb3IpflxuICAgICAgICAgICAgICAgICAgICAgICAgICBUcmVhdG1lbnQsXG4gICAgICAgICAgICAgICAgICAgICAgICBkYXRhPWRpZ19jYWxjKVxuc3VtbWFyeShjb3hfc2ltcF9kaWcpXG5gYGBcbmBgYCJ9 -->
```r
```r
cox_simp_dig<-coxph(Surv(NumDaysAlive,
Censor)~
Treatment,
data=dig_calc)
summary(cox_simp_dig)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiQ2FsbDpcbmNveHBoKGZvcm11bGEgPSBTdXJ2KE51bURheXNBbGl2ZSwgQ2Vuc29yKSB+IFRyZWF0bWVudCwgZGF0YSA9IGRpZ19jYWxjKVxuXG4gIG49IDEwOCwgbnVtYmVyIG9mIGV2ZW50cz0gMTA4IFxuXG4gICAgICAgICAgICAgICAgIGNvZWYgZXhwKGNvZWYpIHNlKGNvZWYpICAgICAgeiBQcig+fHp8KSAgICBcblRyZWF0bWVudERpZyAgLTAuOTQ3MSAgICAwLjM4NzkgICAwLjI0NjEgLTMuODQ4IDAuMDAwMTE5ICoqKlxuVHJlYXRtZW50VHJpbSAtMC4zODc4ICAgIDAuNjc4NiAgIDAuMjM3MSAtMS42MzYgMC4xMDE5MzcgICAgXG4tLS1cblNpZ25pZi4gY29kZXM6ICAwIOKAmCoqKuKAmSAwLjAwMSDigJgqKuKAmSAwLjAxIOKAmCrigJkgMC4wNSDigJgu4oCZIDAuMSDigJgg4oCZIDFcblxuICAgICAgICAgICAgICBleHAoY29lZikgZXhwKC1jb2VmKSBsb3dlciAuOTUgdXBwZXIgLjk1XG5UcmVhdG1lbnREaWcgICAgIDAuMzg3OSAgICAgIDIuNTc4ICAgIDAuMjM5NCAgICAwLjYyODRcblRyZWF0bWVudFRyaW0gICAgMC42Nzg2ICAgICAgMS40NzQgICAgMC40MjY0ICAgIDEuMDgwMFxuXG5Db25jb3JkYW5jZT0gMC42NjQgIChzZSA9IDAuMDMzIClcbkxpa2VsaWhvb2QgcmF0aW8gdGVzdD0gMTUuMTggIG9uIDIgZGYsICAgcD01ZS0wNFxuV2FsZCB0ZXN0ICAgICAgICAgICAgPSAxNC45MyAgb24gMiBkZiwgICBwPTZlLTA0XG5TY29yZSAobG9ncmFuaykgdGVzdCA9IDE1LjY3ICBvbiAyIGRmLCAgIHA9NGUtMDRcbiJ9 -->
Call: coxph(formula = Surv(NumDaysAlive, Censor) ~ Treatment, data = dig_calc)
n= 108, number of events= 108
coef exp(coef) se(coef) z Pr(>|z|) TreatmentDig -0.9471 0.3879 0.2461 -3.848 0.000119 *** TreatmentTrim
-0.3878 0.6786 0.2371 -1.636 0.101937
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05
‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95TreatmentDig 0.3879 2.578 0.2394 0.6284 TreatmentTrim 0.6786 1.474 0.4264 1.0800
Concordance= 0.664 (se = 0.033 ) Likelihood ratio test= 15.18 on 2 df, p=5e-04 Wald test = 14.93 on 2 df, p=6e-04 Score (logrank) test = 15.67 on 2 df, p=4e-04
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQoY294X3NpbXBfZGlnKVxuYGBgXG5gYGAifQ== -->
```r
```r
print(cox_simp_dig)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiQ2FsbDpcbmNveHBoKGZvcm11bGEgPSBTdXJ2KE51bURheXNBbGl2ZSwgQ2Vuc29yKSB+IFRyZWF0bWVudCwgZGF0YSA9IGRpZ19jYWxjKVxuXG4gICAgICAgICAgICAgICAgIGNvZWYgZXhwKGNvZWYpIHNlKGNvZWYpICAgICAgeiAgICAgICAgcFxuVHJlYXRtZW50RGlnICAtMC45NDcxICAgIDAuMzg3OSAgIDAuMjQ2MSAtMy44NDggMC4wMDAxMTlcblRyZWF0bWVudFRyaW0gLTAuMzg3OCAgICAwLjY3ODYgICAwLjIzNzEgLTEuNjM2IDAuMTAxOTM3XG5cbkxpa2VsaWhvb2QgcmF0aW8gdGVzdD0xNS4xOCAgb24gMiBkZiwgcD0wLjAwMDUwNTVcbm49IDEwOCwgbnVtYmVyIG9mIGV2ZW50cz0gMTA4IFxuIn0= -->
Call: coxph(formula = Surv(NumDaysAlive, Censor) ~ Treatment, data = dig_calc)
coef exp(coef) se(coef) z pTreatmentDig -0.9471 0.3879 0.2461 -3.848 0.000119 TreatmentTrim -0.3878 0.6786 0.2371 -1.636 0.101937
Likelihood ratio test=15.18 on 2 df, p=0.0005055 n= 108, number of events= 108
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJlZGljdChjb3hfc2ltcF9kaWcpXG5gYGBcbmBgYCJ9 -->
```r
```r
predict(cox_simp_dig)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
[1] -0.3877584 -0.3877584 -0.9470655 -0.9470655 0.0000000 0.0000000 0.0000000 0.0000000 -0.3877584 -0.3877584 -0.9470655 [12] -0.9470655 -0.3877584 -0.3877584 -0.9470655 -0.9470655 0.0000000 0.0000000 0.0000000 0.0000000 -0.3877584 -0.3877584 [23] -0.9470655 -0.9470655 -0.9470655 -0.9470655 -0.3877584 -0.3877584 0.0000000 0.0000000 -0.9470655 -0.9470655 0.0000000 [34] 0.0000000 -0.3877584 -0.3877584 -0.3877584 -0.3877584 -0.9470655 -0.9470655 0.0000000 0.0000000 -0.3877584 -0.3877584 [45] 0.0000000 0.0000000 -0.9470655 -0.9470655 -0.9470655 -0.9470655 0.0000000 0.0000000 -0.3877584 -0.3877584 -0.9470655 [56] -0.9470655 0.0000000 0.0000000 -0.3877584 -0.3877584 -0.3877584 -0.3877584 -0.9470655 -0.9470655 0.0000000 0.0000000 [67] 0.0000000 0.0000000 -0.9470655 -0.9470655 -0.3877584 -0.3877584 -0.9470655 -0.9470655 0.0000000 0.0000000 -0.3877584 [78] -0.3877584 -0.9470655 -0.9470655 0.0000000 0.0000000 -0.3877584 -0.3877584 -0.9470655 -0.9470655 -0.3877584 -0.3877584 [89] 0.0000000 0.0000000 0.0000000 0.0000000 -0.9470655 -0.9470655 -0.3877584 -0.3877584 0.0000000 0.0000000 -0.3877584 [100] -0.3877584 -0.9470655 -0.9470655 -0.3877584 -0.3877584 -0.9470655 -0.9470655 0.0000000 0.0000000
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGlzdChwcmVkaWN0KGNveF9zaW1wX2RpZykpXG5gYGBcbmBgYCJ9 -->
```r
```r
hist(predict(cox_simp_dig))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5jb3hfc2ltcF93YXRlcjwtY294cGgoU3VydihOdW1EYXlzQWxpdmUsXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgIENlbnNvcil+XG4gICAgICAgICAgICAgICAgICAgICAgICAgIFRyZWF0bWVudCpDb21wZXRpdGlvbixcbiAgICAgICAgICAgICAgICAgICAgICAgIGRhdGE9d2F0ZXJfY2FsYylcbnN1bW1hcnkoY294X3NpbXBfd2F0ZXIpXG5gYGBcbmBgYCJ9 -->
```r
```r
cox_simp_water<-coxph(Surv(NumDaysAlive,
Censor)~
Treatment*Competition,
data=water_calc)
summary(cox_simp_water)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Call: coxph(formula = Surv(NumDaysAlive, Censor) ~ Treatment * Competition, data = water_calc)
n= 336, number of events= 336
coef exp(coef) se(coef) z Pr(>|z|) TreatmentFertilizer -0.43042 0.65024 0.18828 -2.286 0.02225 *
TreatmentWater -0.23675 0.78919 0.19117 -1.238 0.21557
CompetitionWeed Cloth -0.54653 0.57896 0.19079 -2.865 0.00418 **
TreatmentFertilizer:CompetitionWeed Cloth 0.34954 1.41842 0.26778 1.305
0.19178
TreatmentWater:CompetitionWeed Cloth 0.03742 1.03812 0.27004 0.139
0.88980
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05
‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95TreatmentFertilizer 0.6502 1.5379 0.4496 0.9404 TreatmentWater 0.7892 1.2671 0.5426 1.1479 CompetitionWeed Cloth 0.5790 1.7272 0.3983 0.8415 TreatmentFertilizer:CompetitionWeed Cloth 1.4184 0.7050 0.8392 2.3974 TreatmentWater:CompetitionWeed Cloth 1.0381 0.9633 0.6115 1.7624
Concordance= 0.586 (se = 0.023 ) Likelihood ratio test= 18.86 on 5 df, p=0.002 Wald test = 19.67 on 5 df, p=0.001 Score (logrank) test = 20.14 on 5 df, p=0.001
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQoY294X3NpbXBfd2F0ZXIpXG5gYGBcbmBgYCJ9 -->
```r
```r
print(cox_simp_water)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Call: coxph(formula = Surv(NumDaysAlive, Censor) ~ Treatment * Competition, data = water_calc)
coef exp(coef) se(coef) z pTreatmentFertilizer -0.43042 0.65024 0.18828 -2.286 0.02225 TreatmentWater -0.23675 0.78919 0.19117 -1.238 0.21557 CompetitionWeed Cloth -0.54653 0.57896 0.19079 -2.865 0.00418 TreatmentFertilizer:CompetitionWeed Cloth 0.34954 1.41842 0.26778 1.305 0.19178 TreatmentWater:CompetitionWeed Cloth 0.03742 1.03812 0.27004 0.139 0.88980
Likelihood ratio test=18.86 on 5 df, p=0.00204 n= 336, number of events= 336
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJlZGljdChjb3hfc2ltcF93YXRlcilcbmBgYFxuYGBgIn0= -->
```r
```r
predict(cox_simp_water)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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-->
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<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGlzdChwcmVkaWN0KGNveF9zaW1wX3dhdGVyKSlcbmBgYFxuYGBgIn0= -->
```r
```r
hist(predict(cox_simp_water))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
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" />
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Now make a full model using random effects
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY294X2Z1bGxfZGlnPC1jb3htZShTdXJ2KE51bURheXNBbGl2ZSxDZW5zb3IpflxuICAgICAgICAgICAgICAgICAgICAgICAgVHJlYXRtZW50K1xuICAgICAgICAgICAgICAgICAgICAgICAgKDF8Um93KSxcbiAgICAgICAgICAgICAgICAgICAgICBkYXRhPWRpZ19jYWxjKVxuc3VtbWFyeShjb3hfZnVsbF9kaWcpXG5gYGBcbmBgYCJ9 -->
```r
```r
cox_full_dig<-coxme(Surv(NumDaysAlive,Censor)~
Treatment+
(1|Row),
data=dig_calc)
summary(cox_full_dig)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: dig_calc events, n = 108, 108 Iterations= 5 27 NULL Integrated Fitted Log-likelihood -400.9309 -393.3426 -393.337
Chisq df p AIC BICIntegrated loglik 15.18 3.00 0.00167170 9.18 1.13 Penalized loglik 15.19 2.01 0.00050833 11.17 5.79
Model: Surv(NumDaysAlive, Censor) ~ Treatment + (1 | Row) Fixed coefficients coef exp(coef) se(coef) z p TreatmentDig -0.9470335 0.3878900 0.2461466 -3.85 0.00012 TreatmentTrim -0.3877064 0.6786115 0.2370849 -1.64 0.10000
Random effects Group Variable Std Dev Variance
Row Intercept 9.042941e-03 8.177478e-05
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQoY294X2Z1bGxfZGlnKVxuYGBgXG5gYGAifQ== -->
```r
```r
print(cox_full_dig)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: dig_calc events, n = 108, 108 Iterations= 5 27 NULL Integrated Fitted Log-likelihood -400.9309 -393.3426 -393.337
Chisq df p AIC BICIntegrated loglik 15.18 3.00 0.00167170 9.18 1.13 Penalized loglik 15.19 2.01 0.00050833 11.17 5.79
Model: Surv(NumDaysAlive, Censor) ~ Treatment + (1 | Row) Fixed coefficients coef exp(coef) se(coef) z p TreatmentDig -0.9470335 0.3878900 0.2461466 -3.85 0.00012 TreatmentTrim -0.3877064 0.6786115 0.2370849 -1.64 0.10000
Random effects Group Variable Std Dev Variance
Row Intercept 9.042941e-03 8.177478e-05
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJlZGljdChjb3hfZnVsbF9kaWcpXG5gYGBcbmBgYCJ9 -->
```r
```r
predict(cox_full_dig)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
[1] -3.873245e-01 -3.873245e-01 -9.466516e-01 -9.466516e-01 3.818727e-04 3.818727e-04 3.818727e-04 3.818727e-04 [9] -3.873245e-01 -3.873245e-01 -9.466516e-01 -9.466516e-01 -3.873245e-01 -3.873245e-01 -9.466516e-01 -9.466516e-01 [17] 3.818727e-04 3.818727e-04 9.733045e-06 9.733045e-06 -3.876967e-01 -3.876967e-01 -9.470237e-01 -9.470237e-01 [25] -9.470237e-01 -9.470237e-01 -3.876967e-01 -3.876967e-01 9.733045e-06 9.733045e-06 -9.470237e-01 -9.470237e-01 [33] 9.733045e-06 9.733045e-06 -3.876967e-01 -3.876967e-01 -3.880021e-01 -3.880021e-01 -9.473291e-01 -9.473291e-01 [41] -2.956519e-04 -2.956519e-04 -3.880021e-01 -3.880021e-01 -2.956519e-04 -2.956519e-04 -9.473291e-01 -9.473291e-01 [49] -9.473291e-01 -9.473291e-01 -2.956519e-04 -2.956519e-04 -3.880021e-01 -3.880021e-01 -9.471247e-01 -9.471247e-01 [57] -9.123620e-05 -9.123620e-05 -3.877977e-01 -3.877977e-01 -3.877977e-01 -3.877977e-01 -9.471247e-01 -9.471247e-01 [65] -9.123620e-05 -9.123620e-05 -9.123620e-05 -9.123620e-05 -9.471247e-01 -9.471247e-01 -3.877977e-01 -3.877977e-01 [73] -9.468196e-01 -9.468196e-01 2.138814e-04 2.138814e-04 -3.874925e-01 -3.874925e-01 -9.468196e-01 -9.468196e-01 [81] 2.138814e-04 2.138814e-04 -3.874925e-01 -3.874925e-01 -9.468196e-01 -9.468196e-01 -3.874925e-01 -3.874925e-01 [89] 2.138814e-04 2.138814e-04 -2.185990e-04 -2.185990e-04 -9.472521e-01 -9.472521e-01 -3.879250e-01 -3.879250e-01 [97] -2.185990e-04 -2.185990e-04 -3.879250e-01 -3.879250e-01 -9.472521e-01 -9.472521e-01 -3.879250e-01 -3.879250e-01 [105] -9.472521e-01 -9.472521e-01 -2.185990e-04 -2.185990e-04
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGlzdChwcmVkaWN0KGNveF9mdWxsX2RpZykpXG5gYGBcbmBgYCJ9 -->
```r
```r
hist(predict(cox_full_dig))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5jb3hfZnVsbF9zaGFkZTwtY294bWUoU3VydihOdW1EYXlzQWxpdmUsQ2Vuc29yKX5cbiAgICAgICAgICAgICAgICAgICAgICAgIFRyZWF0bWVudCtcbiAgICAgICAgICAgICAgICAgICAgICAgICgxfFBsb3QpLFxuICAgICAgICAgICAgICAgICAgICAgIGRhdGE9c2hhZGVfY2FsYylcbnN1bW1hcnkoY294X2Z1bGxfc2hhZGUpXG5gYGBcbmBgYCJ9 -->
```r
```r
cox_full_shade<-coxme(Surv(NumDaysAlive,Censor)~
Treatment+
(1|Plot),
data=shade_calc)
summary(cox_full_shade)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: shade_calc events, n = 88, 88 Iterations= 10 54 NULL Integrated Fitted Log-likelihood -309.1642 -286.0834 -271.7816
Chisq df p AIC BICIntegrated loglik 46.16 2.00 9.4656e-11 42.16 37.21 Penalized loglik 74.77 9.61 3.4694e-12 55.54 31.73
Model: Surv(NumDaysAlive, Censor) ~ Treatment + (1 | Plot) Fixed coefficients coef exp(coef) se(coef) z p TreatmentShade 1.315212 3.725541 0.2646862 4.97 6.7e-07
Random effects Group Variable Std Dev Variance Plot Intercept 0.9414371 0.8863038
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQoY294X2Z1bGxfc2hhZGUpXG5gYGBcbmBgYCJ9 -->
```r
```r
print(cox_full_shade)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: shade_calc events, n = 88, 88 Iterations= 10 54 NULL Integrated Fitted Log-likelihood -309.1642 -286.0834 -271.7816
Chisq df p AIC BICIntegrated loglik 46.16 2.00 9.4656e-11 42.16 37.21 Penalized loglik 74.77 9.61 3.4694e-12 55.54 31.73
Model: Surv(NumDaysAlive, Censor) ~ Treatment + (1 | Plot) Fixed coefficients coef exp(coef) se(coef) z p TreatmentShade 1.315212 3.725541 0.2646862 4.97 6.7e-07
Random effects Group Variable Std Dev Variance Plot Intercept 0.9414371 0.8863038
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJlZGljdChjb3hfZnVsbF9zaGFkZSlcbmBgYFxuYGBgIn0= -->
```r
```r
predict(cox_full_shade)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
[1] 0.95842523 0.95842523 0.95842523 0.95842523 -0.35678686 -0.35678686 -0.35678686 -0.35678686 -1.64045073 -1.64045073 [11] -1.64045073 -1.64045073 -0.32523864 -0.32523864 -0.32523864 -0.32523864 -0.98488678 -0.98488678 0.33032532 0.33032532 [21] 0.33032532 0.33032532 -0.98488678 -0.98488678 1.39323454 1.39323454 0.07802245 0.07802245 1.39323454 1.39323454 [31] 0.07802245 0.07802245 -0.17105175 -0.17105175 1.14416034 1.14416034 1.14416034 1.14416034 -0.17105175 -0.17105175 [41] 0.76414037 0.76414037 -0.55107172 -0.55107172 -0.55107172 -0.55107172 0.76414037 0.76414037 2.48570789 2.48570789 [51] 2.48570789 2.48570789 1.17049580 1.17049580 1.17049580 1.17049580 0.46581774 0.46581774 1.78102983 1.78102983 [61] 1.78102983 1.78102983 0.46581774 0.46581774 0.03821320 0.03821320 1.35342529 1.35342529 0.03821320 0.03821320 [71] 1.35342529 1.35342529 2.25726820 2.25726820 0.94205611 0.94205611 0.94205611 0.94205611 2.25726820 2.25726820 [81] 2.32485464 2.32485464 2.32485464 2.32485464 1.00964255 1.00964255 1.00964255 1.00964255
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGlzdChwcmVkaWN0KGNveF9mdWxsX3NoYWRlKSlcbmBgYFxuYGBgIn0= -->
```r
```r
hist(predict(cox_full_shade))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5jb3hfZnVsbF93YXRlcjwtY294bWUoU3VydihOdW1EYXlzQWxpdmUsQ2Vuc29yKX5cbiAgICAgICAgICAgICAgICAgICAgICAgIFRyZWF0bWVudCpDb21wZXRpdGlvbitcbiAgICAgICAgICAgICAgICAgICAgICAgICgxfFBsb3QpLFxuICAgICAgICAgICAgICAgICAgICAgIGRhdGE9d2F0ZXJfY2FsYylcbnN1bW1hcnkoY294X2Z1bGxfd2F0ZXIpXG5gYGBcbmBgYCJ9 -->
```r
```r
cox_full_water<-coxme(Surv(NumDaysAlive,Censor)~
Treatment*Competition+
(1|Plot),
data=water_calc)
summary(cox_full_water)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: water_calc events, n = 336, 336 Iterations= 7 39 NULL Integrated Fitted Log-likelihood -1622.377 -1585.374 -1551.377
Chisq df p AIC BICIntegrated loglik 74.01 6.00 6.1506e-14 62.01 39.10 Penalized loglik 142.00 26.96 0.0000e+00 88.08 -14.83
Model: Surv(NumDaysAlive, Censor) ~ Treatment * Competition + (1 | Plot) Fixed coefficients coef exp(coef) se(coef) z p TreatmentFertilizer -0.356679651 0.6999967 0.1949290 -1.83 0.0670 TreatmentWater -0.167131538 0.8460883 0.2001558 -0.84 0.4000 CompetitionWeed Cloth -0.512966998 0.5987165 0.1984176 -2.59 0.0097 TreatmentFertilizer:CompetitionWeed Cloth 0.257533331 1.2937349 0.2774688 0.93 0.3500 TreatmentWater:CompetitionWeed Cloth 0.004541906 1.0045522 0.2790333 0.02 0.9900
Random effects Group Variable Std Dev Variance Plot Intercept 0.6175634 0.3813846
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQoY294X2Z1bGxfd2F0ZXIpXG5gYGBcbmBgYCJ9 -->
```r
```r
print(cox_full_water)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: water_calc events, n = 336, 336 Iterations= 7 39 NULL Integrated Fitted Log-likelihood -1622.377 -1585.374 -1551.377
Chisq df p AIC BICIntegrated loglik 74.01 6.00 6.1506e-14 62.01 39.10 Penalized loglik 142.00 26.96 0.0000e+00 88.08 -14.83
Model: Surv(NumDaysAlive, Censor) ~ Treatment * Competition + (1 | Plot) Fixed coefficients coef exp(coef) se(coef) z p TreatmentFertilizer -0.356679651 0.6999967 0.1949290 -1.83 0.0670 TreatmentWater -0.167131538 0.8460883 0.2001558 -0.84 0.4000 CompetitionWeed Cloth -0.512966998 0.5987165 0.1984176 -2.59 0.0097 TreatmentFertilizer:CompetitionWeed Cloth 0.257533331 1.2937349 0.2774688 0.93 0.3500 TreatmentWater:CompetitionWeed Cloth 0.004541906 1.0045522 0.2790333 0.02 0.9900
Random effects Group Variable Std Dev Variance Plot Intercept 0.6175634 0.3813846
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJlZGljdChjb3hfZnVsbF93YXRlcilcbmBgYFxuYGBgIn0= -->
```r
```r
predict(cox_full_water)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
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<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGlzdChwcmVkaWN0KGNveF9mdWxsX3dhdGVyKSlcbmBgYFxuYGBgIn0= -->
```r
```r
hist(predict(cox_full_water))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5jb3hfZnVsbF93YXRlcjE8LWNveG1lKFN1cnYoTnVtRGF5c0FsaXZlLENlbnNvcil+XG4gICAgICAgICAgICAgICAgICAgICAgICBUcmVhdG1lbnQrXG4gICAgICAgICAgICAgICAgICAgICAgICBDb21wZXRpdGlvbitcbiAgICAgICAgICAgICAgICAgICAgICAgICgxfFBsb3QpLFxuICAgICAgICAgICAgICAgICAgICAgIGRhdGE9d2F0ZXJfY2FsYylcbnN1bW1hcnkoY294X2Z1bGxfd2F0ZXIxKVxuYGBgXG5gYGAifQ== -->
```r
```r
cox_full_water1<-coxme(Surv(NumDaysAlive,Censor)~
Treatment+
Competition+
(1|Plot),
data=water_calc)
summary(cox_full_water1)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: water_calc events, n = 336, 336 Iterations= 7 32 NULL Integrated Fitted Log-likelihood -1622.377 -1585.928 -1551.725
Chisq df p AIC BICIntegrated loglik 72.90 4.00 5.5511e-15 64.9 49.63 Penalized loglik 141.31 25.05 0.0000e+00 91.2 -4.43
Model: Surv(NumDaysAlive, Censor) ~ Treatment + Competition + (1 | Plot) Fixed coefficients coef exp(coef) se(coef) z p TreatmentFertilizer -0.2301898 0.7943828 0.1369386 -1.68 0.09300 TreatmentWater -0.1632106 0.8494123 0.1401706 -1.16 0.24000 CompetitionWeed Cloth -0.4224990 0.6554069 0.1157955 -3.65 0.00026
Random effects Group Variable Std Dev Variance Plot Intercept 0.6224706 0.3874696
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQoY294X2Z1bGxfd2F0ZXIxKVxuYGBgXG5gYGAifQ== -->
```r
```r
print(cox_full_water1)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Cox mixed-effects model fit by maximum likelihood Data: water_calc events, n = 336, 336 Iterations= 7 32 NULL Integrated Fitted Log-likelihood -1622.377 -1585.928 -1551.725
Chisq df p AIC BICIntegrated loglik 72.90 4.00 5.5511e-15 64.9 49.63 Penalized loglik 141.31 25.05 0.0000e+00 91.2 -4.43
Model: Surv(NumDaysAlive, Censor) ~ Treatment + Competition + (1 | Plot) Fixed coefficients coef exp(coef) se(coef) z p TreatmentFertilizer -0.2301898 0.7943828 0.1369386 -1.68 0.09300 TreatmentWater -0.1632106 0.8494123 0.1401706 -1.16 0.24000 CompetitionWeed Cloth -0.4224990 0.6554069 0.1157955 -3.65 0.00026
Random effects Group Variable Std Dev Variance Plot Intercept 0.6224706 0.3874696
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJlZGljdChjb3hfZnVsbF93YXRlcjEpXG5gYGBcbmBgYCJ9 -->
```r
```r
predict(cox_full_water1)
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
[1] 0.287979227 0.287979227 -0.134519820 -0.134519820 -0.201498977 -0.201498977 0.451189835 0.451189835 0.028690788 [10] 0.028690788 0.221000070 0.221000070 -0.598292598 -0.598292598 -1.087770802 -1.087770802 -0.435081990 -0.435081990 [19] -0.857581037 -0.857581037 -0.665271755 -0.665271755 -1.020791645 -1.020791645 0.165299598 0.165299598 -0.487389214 [28] -0.487389214 -0.420410056 -0.420410056 -0.257199449 -0.257199449 -0.064890167 -0.064890167 0.002088990 0.002088990 [37] -0.352155801 -0.352155801 -0.285176644 -0.285176644 -0.121966036 -0.121966036 0.300533011 0.300533011 0.070343246 [46] 0.070343246 0.137322403 0.137322403 -0.489804567 -0.489804567 -0.067305521 -0.067305521 -0.556783725 -0.556783725 [55] -0.134284678 -0.134284678 -0.326593960 -0.326593960 0.095905087 0.095905087 -1.700636867 -1.700636867 -1.537426259 [64] -1.537426259 -1.345116977 -1.345116977 -1.767616024 -1.767616024 -1.114927212 -1.114927212 -1.278137820 -1.278137820 [73] -1.616020762 -1.616020762 -1.779231370 -1.779231370 -1.193521715 -1.193521715 -1.423711480 -1.423711480 -1.846210527 [82] -1.846210527 -1.356732323 -1.356732323 -0.878989292 -0.878989292 -1.368467496 -1.368467496 -0.715778684 -0.715778684 [91] -1.301488339 -1.301488339 -0.945968449 -0.945968449 -1.138277731 -1.138277731 -0.318274332 -0.318274332 0.334414479 [100] 0.334414479 0.171203872 0.171203872 -0.251295175 -0.251295175 0.104224714 0.104224714 -0.088084567 -0.088084567 [109] 0.092935298 0.092935298 -0.492774357 -0.492774357 -0.329563749 -0.329563749 -0.070275310 -0.070275310 -0.559753514 [118] -0.559753514 -0.137254467 -0.137254467 0.127828743 0.127828743 -0.294670304 -0.294670304 -0.131459696 -0.131459696 [127] -0.361649461 -0.361649461 0.291039351 0.291039351 0.060849586 0.060849586 -0.704071754 -0.704071754 -0.444783315 [136] -0.444783315 -0.934261519 -0.934261519 -0.867282362 -0.867282362 -0.281572707 -0.281572707 -0.511762472 -0.511762472 [145] -0.687841107 -0.687841107 -0.102131452 -0.102131452 -0.332321217 -0.332321217 -0.524630499 -0.524630499 -0.265342060 [154] -0.265342060 -0.754820264 -0.754820264 -0.984128641 -0.984128641 -0.628608751 -0.628608751 -0.398418986 -0.398418986 [163] -0.820918033 -0.820918033 -0.561629594 -0.561629594 -1.051107798 -1.051107798 -0.801357593 -0.801357593 -1.156877483 [172] -1.156877483 -0.993666875 -0.993666875 -1.156877483 -1.156877483 -0.801357593 -0.801357593 -0.571167828 -0.571167828 [181] -1.279379963 -1.279379963 -1.116169355 -1.116169355 -0.923860073 -0.923860073 -0.856880916 -0.856880916 -1.346359120 [190] -1.346359120 -0.693670308 -0.693670308 -1.145711694 -1.145711694 -1.308922302 -1.308922302 -0.723212648 -0.723212648 [199] -0.886423255 -0.886423255 -1.375901460 -1.375901460 -0.953402413 -0.953402413 -0.586550382 -0.586550382 -0.778859664 [208] -0.778859664 -0.942070272 -0.942070272 -0.519571225 -0.519571225 -1.009049429 -1.009049429 -0.356360617 -0.356360617 [217] 0.440380046 0.440380046 -0.145329609 -0.145329609 -0.212308766 -0.212308766 0.210190281 0.210190281 0.017880999 [226] 0.017880999 0.277169438 0.277169438 0.581023781 0.581023781 -0.071665031 -0.071665031 -0.004685874 -0.004685874 [235] 0.350834016 0.350834016 0.158524734 0.158524734 0.417813173 0.417813173 -0.128611195 -0.128611195 0.293887852 [244] 0.293887852 0.360867009 0.360867009 0.524077617 0.524077617 -0.061632038 -0.061632038 0.101578570 0.101578570 [253] 0.698447498 0.698447498 0.468257733 0.468257733 0.535236891 0.535236891 0.275948452 0.275948452 0.112737844 [262] 0.112737844 0.045758687 0.045758687 0.083799780 0.083799780 -0.338699267 -0.338699267 0.247010387 0.247010387 [271] -0.175488660 -0.175488660 -0.405678425 -0.405678425 0.016820622 0.016820622 0.439621351 0.439621351 -0.213067461 [280] -0.213067461 0.209431586 0.209431586 0.276410743 0.276410743 0.017122304 0.017122304 -0.146088304 -0.146088304 [289] 0.219869324 0.219869324 0.027560042 0.027560042 -0.135650566 -0.135650566 0.286848481 0.286848481 0.450059089 [298] 0.450059089 -0.202629723 -0.202629723 -0.688166791 -0.688166791 -0.457977026 -0.457977026 -0.621187634 -0.621187634 [307] -0.198688587 -0.198688587 -0.265667745 -0.265667745 -0.035477979 -0.035477979 -0.072037612 -0.072037612 0.350461435 [316] 0.350461435 0.513672043 0.513672043 0.091172996 0.091172996 -0.139016769 -0.139016769 0.283482278 0.283482278 [325] 0.832503048 0.832503048 0.765523890 0.765523890 0.410004001 0.410004001 0.343024844 0.343024844 0.995713655 [334] 0.995713655 0.573214609 0.573214609
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGlzdChwcmVkaWN0KGNveF9mdWxsX3dhdGVyMSkpXG5gYGBcbmBgYCJ9 -->
```r
```r
hist(predict(cox_full_water1))
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5jb2VmX3ZhbHVlcyA8LSBjKC0wLjIzMDE4OTgsIC0wLjE2MzIxMDYsIC0wLjQyMjQ5OTApXG5zZV92YWx1ZXMgPC0gYygwLjEzNjkzODYsIDAuMTQwMTcwNiwgMC4xMTU3OTU1KVxuXG4jIENhbGN1bGF0ZSB6IGFuZCBwIHZhbHVlc1xuel92YWx1ZXMgPC0gY29lZl92YWx1ZXMgLyBzZV92YWx1ZXNcbnBfdmFsdWVzIDwtIDIgKiAoMSAtIHBub3JtKGFicyh6X3ZhbHVlcykpKVxuXG4jIFByaW50IHRoZSBjYWxjdWxhdGVkIHogYW5kIHAgdmFsdWVzXG56X3ZhbHVlc1xuYGBgXG5gYGAifQ== -->
```r
```r
coef_values <- c(-0.2301898, -0.1632106, -0.4224990)
se_values <- c(0.1369386, 0.1401706, 0.1157955)
# Calculate z and p values
z_values <- coef_values / se_values
p_values <- 2 * (1 - pnorm(abs(z_values)))
# Print the calculated z and p values
z_values
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIC0xLjY4MDk3MSAtMS4xNjQzNzEgLTMuNjQ4NjY1XG4ifQ== -->
[1] -1.680971 -1.164371 -3.648665
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucF92YWx1ZXNcbmBgYFxuYGBgIn0= -->
```r
```r
p_values
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuMDkyNzY4NTY5NCAwLjI0NDI3MzY0MTggMC4wMDAyNjM2MDY1XG4ifQ== -->
[1] 0.0927685694 0.2442736418 0.0002636065
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuMS1leHAoY29lZihjb3hfZnVsbF93YXRlcjEpKVxuYGBgXG5gYGAifQ== -->
```r
```r
1-exp(coef(cox_full_water1))
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICBUcmVhdG1lbnRGZXJ0aWxpemVyICAgICAgICBUcmVhdG1lbnRXYXRlciBDb21wZXRpdGlvbldlZWQgQ2xvdGggXG4gICAgICAgICAgICAwLjIwNTYxNzIgICAgICAgICAgICAgMC4xNTA1ODc3ICAgICAgICAgICAgIDAuMzQ0NTkzMSBcbiJ9 -->
TreatmentFertilizer TreatmentWater CompetitionWeed Cloth 0.2056172 0.1505877 0.3445931
<!-- rnb-output-end -->
<!-- rnb-source-begin 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 -->
```r
```r
cox_full_water_notreatment<-coxme(Surv(NumDaysAlive,Censor)~
Competition+
(1|Plot),
data=water_calc)
cox_full_water_nocompetition<-coxme(Surv(NumDaysAlive,Censor)~
Treatment+
(1|Plot),
data=water_calc)
anova(cox_full_water1,cox_full_water_notreatment) # effect of treatment (using cox_full_water1 here assuming you have no interaction effect)
<!-- rnb-source-end -->
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Analysis of Deviance Table Cox model: response is Surv(NumDaysAlive,
Censor) Model 1: ~Treatment + Competition + (1 | Plot) Model 2:
~Competition + (1 | Plot) loglik Chisq Df P(>|Chi|) 1 -1585.9
2 -1587.4 2.9378 2 0.2302
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```r
```r
anova(cox_full_water1,cox_full_water_nocompetition) # effect of competition
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Analysis of Deviance Table Cox model: response is Surv(NumDaysAlive,
Censor) Model 1: ~Treatment + Competition + (1 | Plot) Model 2:
~Treatment + (1 | Plot) loglik Chisq Df P(>|Chi|)
1 -1585.9
2 -1592.6 13.306 1 0.0002646 *** — Signif. codes: 0 ‘’ 0.001
‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
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```r
```r
#Now if you want to do a post-hoc test of your different Treatment levels, you can use this:
library(multcomp)
summary(glht(cox_full_water1, mcp(Treatment=\Tukey\))) # for treatment
<!-- rnb-source-end -->
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Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: coxme(formula = Surv(NumDaysAlive, Censor) ~ Treatment + Competition + (1 | Plot), data = water_calc)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Fertilizer - Control == 0 -0.23019 0.13694 -1.681 0.212 Water - Control == 0 -0.16321 0.14017 -1.164 0.475 Water - Fertilizer == 0 0.06698 0.14015 0.478 0.882 (Adjusted p values reported – single-step method)
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```r
```r
summary(glht(cox_full_water1, mcp(Competition=\Tukey\))) # for competition – you have only 2 levels so this should give you the same result as the main effect.
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Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: coxme(formula = Surv(NumDaysAlive, Censor) ~ Treatment + Competition + (1 | Plot), data = water_calc)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|)
Weed Cloth - No Cloth == 0 -0.4225 0.1158 -3.649 0.000264 *** — Signif.
codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’
1 (Adjusted p values reported – single-step method)
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<!-- rnb-text-begin -->
Now we can compare the models to see which model is best
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
anova(cox_simp_dig,cox_full_dig)
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Analysis of Deviance Table Cox model: response is Surv(NumDaysAlive,
Censor) Model 1: ~Treatment Model 2: ~Treatment + (1 | Row) loglik Chisq
Df P(>|Chi|) 1 -393.34
2 -393.34 0.003 1 0.9561
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```r
```r
AIC(cox_simp_dig,cox_full_dig) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here the AIC scores are the same, so parsimony says to keep the simple model.
anova(cox_simp_shade,cox_full_shade)
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Analysis of Deviance Table Cox model: response is Surv(NumDaysAlive,
Censor) Model 1: ~Treatment Model 2: ~Treatment + (1 | Plot) loglik
Chisq Df P(>|Chi|)
1 -299.75
2 -286.08 27.328 1 1.717e-07 *** — Signif. codes: 0 ‘’ 0.001
‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
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```r
```r
AIC(cox_simp_shade,cox_full_shade) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here it is the full model because the AIC score is smaller.
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```r
```r
anova(cox_simp_water,cox_full_water1,cox_full_water)
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Analysis of Deviance Table Cox model: response is Surv(NumDaysAlive,
Censor) Model 1: ~Treatment * Competition Model 2: ~Treatment +
Competition + (1 | Plot) Model 3: ~Treatment * Competition + (1 | Plot)
loglik Chisq Df P(>|Chi|)
1 -1613.0
2 -1585.9 54.0372 1 1.967e-13 *** 3 -1585.4 1.1079 2 0.5747
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05
‘.’ 0.1 ‘ ’ 1
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```r
```r
AIC(cox_simp_water,cox_full_water1,cox_full_water) #But comparing the AIC scores is easiest. Use the middle model (cox_full_water1) with the lowest AIC.
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```r
```r
anova(cox_full_water,cox_full_water1)
<!-- rnb-source-end -->
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Analysis of Deviance Table Cox model: response is Surv(NumDaysAlive,
Censor) Model 1: ~Treatment * Competition + (1 | Plot) Model 2:
~Treatment + Competition + (1 | Plot) loglik Chisq Df P(>|Chi|) 1
-1585.4
2 -1585.9 1.1079 2 0.5747
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```r
```r
summary(glht(cox_full_water, mcp(Competition:Treatment=\Tukey\)))
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Error: unexpected ‘=’ in (glht(cox_full_water ```
---
title: "Relative fitness in a field setting"
output: html_notebook
date: December 2022
authors: Miranda Melen, Nicky Lustenhouwer
---
#Background
At Blue Oak Ranch Reserve, we established a 10m x 26m fenced field site to test whether evolution over the course of invasion away from roads has resulted in enhanced performance in undisturbed vegetation relative to roadside populations. The experiment was replicated in a randomized block design (20 plots in total). Each plot was 1.5m2 with 16 D. graveolens growing in a 4 x 4 grid centered on the plot. There was 33cm between each plant and 25cm between the edge plants and the border of the plot.

The experiment included multiple treatments; however, only the two most relevant to the focus of this paper are included here. We tested whether plant genotypes collected from the two habitats responded differently to the disturbance of biomass removal. We tilled in December 2020 to completely remove below and aboveground biomass, and then weeded to remove aboveground biomass throughout the growing season. In contrast, we left the control plots untouched, allowing the previous year’s thatch to persist and background vegetation to grow throughout the experiment. 

In January 2021, we germinated seeds in Petri dishes at the UCSC Coastal Science Campus greenhouse incubation chambers before transplanting them into field-collected soil (collected in late December 2020 from Blue Oak Ranch Reserve). Seedlings grew in the greenhouse for about eight weeks until all plants had their first two true leaves emerge and lengthen. Ideally, we would have placed seeds directly into the field, but to maximize biosafety, we used seedling transplants that could be tracked with 100% certainty.

We measured the longest leaf for each plant and then transplanted them into the ground in late February 2021 at Blue Oak Ranch Reserve. During the first month of growth, we replaced any D. graveolens that died. We conducted weekly phenology surveys to assess D. graveolens plant health, and at the first sign of buds, we measured plant height and harvested the aboveground biomass by cutting at the root crown and drying in a 60ºC oven for 3 days before weighing.

#Data Analysis
Statistical analyses were performed in R version 4.2.1 (R Core Team 2022) using linear mixed-effects models with the lme4 (Bates et al. 2015), lmerTest (Kuznetsova et al. 2017), and DHARMa packages (Hartig 2022), generalized linear mixed models with the glmmTMB package (Brooks et al. 2017), and mixed effects cox models with the coxme (Therneau 2022a) and survival (Therneau 2022b) packages.

##Models Included

###Cox proportional hazard models
Here we will use 'coxme' which allows you to conduct mixed effects Cox proportional hazards models. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. We conducted a germination experiment using Dittrichia graveolens seeds on filter paper. "Surv" creates a survival object to combine the days column (NumDaysAlive) and the censor column (Censor) to be used as a response variable in a model formula.The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: "var1 * var2" will give you the interaction term and the individual variables: so "var1 + var2 + var1:var2". To add random effects, type "+ (1|random effect)".

Assumptions for cox models: https://www.theanalysisfactor.com/assumptions-cox-regression/

###Linear mixed-effects models
Linear Mixed Effects models are used for regression analyses involving dependent data. For a tutorial: https://doi.org/10.1177/2515245920960351

fullmodel<-lmer(log(Biomass)~   #Response variable: biomass
          Habitat*Treatment+    #Fixed effects and their interactions(*)
          (1|Site)+(1|Block),   #Random effect with random intercept only
          data=mydata)             #Dataframe
  
###Generalized linear mixed models
under construction


#Libraries
```{r}
#install.packages("coxme")
#install.packages("survival")
#install.packages("ggplot2")
#install.packages("ggfortify")
#install.packages("car")
#install.packages("multcomp")
#install.packages("lme4")
#install.packages("lmerTest")
#install.packages("DHARMa")
#install.packages("dplyr")
#install.packages("emmeans")
#install.packages('TMB', type = 'source')
#install.packages("glmmTMB")
#install.packages("MASS")
#install.packages("emmeans")
#install.packages("AICcmodavg")
library(coxme)
library(survival)
library(ggplot2)
library(ggfortify)
library(car)
library(multcomp)
library(lme4)
library(lmerTest)
library(DHARMa)
library(dplyr)
library(emmeans)
library(TMB)
library(glmmTMB)
library(MASS)
library(emmeans)
library(AICcmodavg)
library(tidyverse)
```

#Load Data
This dataframe has one row per plant (800 observations). Data are for survivorship curves (3 censor options), the number of days the plant stayed alive (NumDaysAlive) and aboveground biomass. Censors with a 1 denote reaching the event (CensorAll = died, CensorBiomass = survived to collect biomass, CensorReproduction = survived to reproduce) and a 0 denoting when a seed didn't germinate by the last census date (Census = 11/15/21). CensorReproduction will be most useful in understanding the amount of biomass produced by an individual when buds appear.
```{r}
mydata<-read.csv("/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/field_relative_fitness_2021.csv",stringsAsFactors=T)
str(mydata) #Check that each column has the right class (factor, integer, numeric, etc.)
mydata$Site<-as.character(mydata$Site)
mydata$Treatment<-factor(mydata$Treatment,
                         levels=c("Grassland","Biomass Removal","Hemizonia","Raking","Raking & Clipping")) #Changing the contrast order so that everything is compared to Grassland (control)
```

#Early Growth
This code uses Growth data with Habitat (roadside and vegetated) and Treatment in a glmm model. Anova and Tukey tests are used on the successful Model4 with the creation of a box plot as a finished product.

Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)

Also Note: Growth data is a measure of growth of longest leaf. Each plant was measured upon transplanting into the field in March and then again in June 2021. This plant has a juvenile stage of a basal rosette, and then it bolts and produced smaller cauline leaves. The goal was to capture early growth data for this plant before bolting occurs so that the measurements capture the basal rosette stage, however in some cases the plants bolted earlier than expected and the resulting measurement was smaller than previous. In these cases we determined that the data should be removed from the dataset as the negative number (or changing it to a zero) does not reflect the biological importance of the measurement.

Here we need to filter the data to remove Growth>0 and to convert plant measurement dates to date format
```{r}
growth_mydata<-mydata%>%filter(Growth>0) #Here I am only looking at the Growth data that is greater than 0 (see Also Note above)
growth_mydata$PlantDate<-as.Date(growth_mydata$PlantDate,"%m/%d/%y") 
growth_mydata$Num.Days.Growth<-as.Date("2021-05-22")-growth_mydata$PlantDate
growth_mydata$Num.Days.Growth<-as.numeric(growth_mydata$Num.Days.Growth)
growth_mydata$Growth.Rate<-growth_mydata$Growth/growth_mydata$Num.Days.Growth #First calculate number of days of growth to get the Rate (Growth/Num.Days.Growing). Then fit data to Beta distribution
```

##Histograms
Original data
```{r}
hist(growth_mydata$Growth.Rate,
     col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data
shapiro.test(growth_mydata$Growth.Rate)
```

Log transform data (https://www.statology.org/transform-data-in-r/)
```{r}
log_growth_mydata<-log10(growth_mydata$Growth.Rate)
hist(log_growth_mydata,
     col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution
shapiro.test(log_growth_mydata) #Data does not improve with log transformation
```

Next I tried a square root transformation, which improved the distribution, but my first model failed the singular fit (see Model 1).
```{r}
sqrt_growth_mydata<-sqrt(growth_mydata$Growth.Rate)
hist(sqrt_growth_mydata,
     col='steelblue',main='Log Transformed') #Square root transformed data looks better than the original distribution
shapiro.test(sqrt_growth_mydata)
```

##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects
```{r}
growth_fullmodel1<-lmer(sqrt(Growth.Rate)~Habitat*Treatment+(1|Site)+(1|Block),data=growth_mydata)
isSingular(growth_fullmodel1,tol=1e-4) #=True
summary(growth_fullmodel1) #Variance explained by Site = 0.000
anova(growth_fullmodel1)
```

Site as a random effect does not explain any of the variance in the model, therefore let's try Site as a fixed effect to see if it adds to the model.

##Model 2 - lmer: Modeling Site as a fixed effect and Block as a random effect
```{r}
growth_fullmodel2<-lmer(log(Growth.Rate)~
                          Habitat*Treatment+
                          Site+
                          (1|Block),
                        data=growth_mydata)
summary(growth_fullmodel2) #As a fixed effect, one of the Sites (OAP) is significant.
anova(growth_fullmodel2) #Site as a fixed effect accounts for 3% of the variance
```

Now we'll look at the QQ plots and the residuals using DHARMa
```{r}
qqnorm(resid(growth_fullmodel2)) #qqplot
qqline(resid(growth_fullmodel2)) #add the line
testDispersion(growth_fullmodel2) #red line should be in the middle of the distribution
myDHARMagraph2<-simulateResiduals(growth_fullmodel2) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph2) #plotting graph. At this point, you don't want any text or lines to be red. The QQ plots are not looking good for this model. Let's try fitting to a negative binomial distribution.
```

##Model 3 - glmer.nb: Negative Binomial
```{r eval=FALSE, include=FALSE}
growth_fullmodel3<-glmer.nb(Growth.Rate~
                              Habitat*Treatment+
                              (1|Site)+
                              (1|Block),
                            data=growth_mydata) #this barfs right now, not sure why
isSingular(growth_fullmodel3,
           tol=1e-4)
#summary(growth_fullmodel3)
#qqnorm(resid(growth_fullmodel3)) #qqplot
#qqline(resid(growth_fullmodel3)) #add the line
#testDispersion(growth_fullmodel3) #red line should be in the middle of the distribution
#myDHARMagraph3<-simulateResiduals(growth_fullmodel3) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
#plot(myDHARMagraph3) #plotting graph.The QQ plots are not looking good for this model.
```

So this model isn't working well either. Let's try building a model and fitting it to a Beta distribution, first without a link function. Check it with DHARMa, and if it doesn't look good, then fit it to a Beta distribution with a logit link function.

##Model 4 - glmm: Beta Distribution
Now I'm using glmm because I'm fitting to other distributions (beta)
```{r}
growth_fullmodel4<-glmmTMB(Growth.Rate~
                             Habitat*Treatment+
                             (1|Site)+
                             (1|Block),
                           family=beta_family(),
                           data=growth_mydata) 
summary(growth_fullmodel4)
qqnorm(resid(growth_fullmodel4)) #qqplot
qqline(resid(growth_fullmodel4)) #add the line
testDispersion(growth_fullmodel4) #red line should be in the middle of the distribution
myDHARMagraph4<-simulateResiduals(growth_fullmodel4) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph4) #plotting graph. Looks good, but check the outliers to make sure they are real.
```

We should check the outliers in the the DHARMa plot to make sure they make sense.
```{r}
growth_outlier_boxplot1<-ggplot(growth_mydata)+
  geom_boxplot(aes(x=Habitat,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray85"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Habitat")
growth_outlier_boxplot1

max(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Away"])
max(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Roadside"]) #Yes, these outliers make sense, so I don't need to worry about the red stars in the DHARMa plot.
```

###Best Model
```{r}
growth_fullmodel4<-glmmTMB(Growth.Rate~
                             Habitat*Treatment+
                             (1|Site)+
                             (1|Block),
                           family=beta_family(),
                           data=growth_mydata)
summary(growth_fullmodel4)
```

###Post-Hoc Test
Remove non-significant interaction terms before running the Tukey.
```{r}
#?emmeans,emmeans(model,pairwise~treatment)
growth_fullmodel4.1<-glmmTMB(Growth.Rate~
                               Treatment+
                               (1|Site)+
                               (1|Block),
                             family=beta_family(),
                             data=growth_mydata)
summary(growth_fullmodel4.1)
emmeans(growth_fullmodel4.1,
        pairwise~Treatment)
```
###Chapter 1 three panel figure
```{r}

```


###ggplot
####Interaction plot
#####Biomass x Treatment: Growth
```{r}
pd<-position_dodge(0)

growth_mydata1<-growth_mydata%>% 
  replace_na(list(Biomass=0))%>%
  filter(CensorReproduction==1)%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(Biomass),
            SD=sd(Biomass),
            N=length(Biomass))%>%
  mutate(SE=SD/sqrt(N))

field_int_bio_grow_gg<-ggplot(growth_mydata1,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  ylab("Biomass (g)")+
  coord_cartesian(ylim = c(0,10))+
  xlab("")+
  #ggtitle("Biomass of Dittrichia graveolens that budded")+
  theme_bw(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        axis.title.y=ggtext::element_markdown(),
        legend.position = c(0.8, 0.8))+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="*Dittrichia graveolens* \n Biomass (g)",
       color="Source Habitat",
       shape="Source Habitat")
field_int_bio_grow_gg

#ggsave(plot=field_int_bio_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_bio_grow_gg.png",width=25,height=15,units="cm",dpi=800)
```

#####Growth Rate x Treatment: Growth
```{r}
pd<-position_dodge(0)

growth_mydata2<-growth_mydata%>% 
  replace_na(list(Growth.Rate=0))%>%
  filter(CensorReproduction==1)%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(Growth.Rate),
            SD=sd(Growth.Rate),
            N=length(Growth.Rate))%>%
  mutate(SE=SD/sqrt(N))

field_int_rate_grow_gg<-ggplot(growth_mydata2,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  coord_cartesian(ylim = c(0,0.5))+
  xlab("")+
  theme_bw(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        axis.title.y=ggtext::element_markdown(),
        legend.position = c(0.8, 0.8))+
 # ggtitle("Growth of Dittrichia graveolens that budded")+
  ylab("*Dittrichia graveolens* \n (Change in Leaf Length/Days)")+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="Change in *Dittrichia graveolens* Leaf Length/Day",
       color="Source Habitat",
       shape="Source Habitat")
field_int_rate_grow_gg

#ggsave(plot=field_int_rate_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_rate_grow_gg.png",width=25,height=15,units="cm",dpi=800)
```


####Boxplot
```{r}
#Boxplot with Early Growth Rate by Habitat
growth_boxplot1<-ggplot(growth_mydata)+
  geom_boxplot(aes(x=Habitat,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray58"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Habitat")
growth_boxplot1

#ggsave(plot=growth_boxplot1,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplot1.png",width=20,height=20,units="cm",dpi=800)

#Boxplot with Early Growth Rate by All Treatments
growth_boxplot2<-ggplot(growth_mydata)+
  geom_boxplot(aes(x=Treatment,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray85"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Treatment")
growth_boxplot2

#ggsave(plot=growth_boxplot2,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplot2.png",width=20,height=20,units="cm",dpi=800)

#Boxplot with Early Growth Rate by 2 treatments (Control and Biomass Removal)
sub_growth_mydata<-subset(growth_mydata,
                          Treatment%in%c('Control','Biomass Removal'))

growth_boxplot3<-ggplot(sub_growth_mydata)+
  geom_boxplot(aes(x=Treatment,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray85"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Treatment")
growth_boxplot3

#ggsave(plot=growth_boxplot3,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplo3.png",width=20,height=20,units="cm",dpi=800)
```

#Reproductive Biomass
This code uses Biomass data with Habitat (roadside and vegetated) and Treatment in a lmer model. ANOVA and Tukey are used on the successful Model 3 with the creation of a box plot as a finished product.

Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)

Also Note: Biomass data is a measure of aboveground biomass of all individuals harvested from the site. In some cases this was before they budded, but we harvested the wilted plant in case that information was needed in the future. Here we only want to look at biomass (well, log(Biomass)) for reproductive individuals.

Here we need to subset the data to only look at Biomass when CensorReproduction = 1, so that all Biomass data is for reproductive individuals only.
```{r}
reproduction_mydata<-subset(mydata,CensorReproduction%in%c('1')) #Here I am only looking at the Biomass data where the CensorReproduction = 1
```


##Histograms
```{r}
hist(reproduction_mydata$Biomass,col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data
shapiro.test(reproduction_mydata$Biomass)
```

Log transform data (https://www.statology.org/transform-data-in-r/)
```{r}
log_reproduction_mydata<-log10(reproduction_mydata$Biomass)
hist(log_reproduction_mydata,col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution
shapiro.test(log_reproduction_mydata)
#Log transformed data has a better distribution than the original data so we will use the log transformed data with our models
```

##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects
```{r}
fullmodel1<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Site)+
                   (1|Block),
                 data=reproduction_mydata)
isSingular(fullmodel1,tol=1e-4)
summary(fullmodel1) #Variance explained by Site = 0.000
anova(fullmodel1)
#Site as a random effect does not explain any of the variance in the model, therefore let's try Site as a fixed effect to demonstrate that it doesn't add to the model.
predict(fullmodel1)
hist(predict(fullmodel1,type="response"))
```

##Model 2 - lmer: Modeling Site as a fixed effect
```{r}
fullmodel2<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   Site+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel2)
anova(fullmodel2)
#Site as a fixed effect is not significant, therefore it should not be used as a fixed effect in addition to it not being used as a random effect.
```

##Model 3 - lmer: Site is removed from this model because it explains very little of the variance
```{r}
fullmodel3<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel3)
anova(fullmodel3)
```

###QQ Plots
Now we'll look at the QQ plots and the residuals using DHARMa
```{r}
qqnorm(resid(fullmodel3)) #qqplot
qqline(resid(fullmodel3)) #add the line
testDispersion(fullmodel3) #red line should be in the middle of the distribution
myDHARMagraph3<-simulateResiduals(fullmodel3) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph3) #plotting graph. At this point, you don't want any text or lines to be red.
```

When we compare the model summaries and Anova of Models 2 & 3, we see that the removal of Site doesn't impact the model. Therefore the more simple Model 3 is a better choice. But let's keep checking...

###Compare AIC Scores
Now I need to compare the AIC scores for all the models to tell me which is the better model (but it will not say which fits my data better, that is why I did all the DHARMa stuff)

Using aictab to make the comparison of models and table 
```{r}
models<-list(fullmodel1,fullmodel2,fullmodel3)
mod.names<-c('Site.Random','Site.Fixed','No.Site')
aictab(cand.set=models,modnames=mod.names)
#The lowest AICc score is listed first and indicates the best fitting model, here, Model 3 (No.Site) where Site is not included. The cut-off for comparing Models is 2 units. The difference between Model 1 (Site.Random) and Model 3 (No.Site) is 2.12. So Model 3 is marginally better than Model 2.
```

##Other Models Attempted
Other ideas that were explored to keep Site but resulted in singular error and Site explaining 0.000 of the variance: 
fullmodel4<-lmer(log(Biomass)~ Treatment + (1|Site) + (1|Block), data = mydata)
summary(fullmodel4)
fullmodel5<-lmer(log(Biomass)~ Treatment + Habitat + (1|Site) + (1|Block), data = mydata)
summary(fullmodel5)

###Best Model
```{r}
fullmodel3<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel3)
```

###Post-Hoc Test
You should remove non-significant interaction terms before running a post-hoc test. It is difficult to judge the main effects (Habitat and Site) when you also have the interaction term present, so when it is not significant, fit a new model without it.
```{r}
fullmodel3.1<-lmer(log(Biomass)~
                     Treatment+
                     (1|Block),
                   data=reproduction_mydata)
summary(fullmodel3.1)
anova(fullmodel3.1)
qqnorm(resid(fullmodel3.1)) #qqplot
qqline(resid(fullmodel3.1)) #add the line
testDispersion(fullmodel3.1) #red line should be in the middle of the distribution
myDHARMagraph3.1<-simulateResiduals(fullmodel3.1) #testing for heteroscedasticity
plot(myDHARMagraph3.1) #plotting graph
```

```{r}
fullmodel3.2<-lmer(log(Biomass)~
                     Treatment+
                     (1|Block)+
                     (1|Population),
                   data=mydata)
summary(fullmodel3.2)
anova(fullmodel3.2)
qqnorm(resid(fullmodel3.2)) #qqplot
qqline(resid(fullmodel3.2)) #add the line
testDispersion(fullmodel3.2) #red line should be in the middle of the distribution
myDHARMagraph3.2<-simulateResiduals(fullmodel3.2) #testing for heteroscedasticity
plot(myDHARMagraph3.2) #plotting graph
```

Let's use emmeans for our Best Model (fullmodel3) and the two off-shoots.
```{r}
#?emmeans, emmeans(model, pairwise ~ treatment)
emmeans(fullmodel3,pairwise~Treatment)
emmeans(fullmodel3.1,pairwise~Treatment)
emmeans(fullmodel3.2,pairwise~Treatment)
#These models result in similar Tukey outcomes
```

##ggplot - lmer
```{r}
biomass_gg<-ggplot(data=reproduction_mydata,
       aes(x=factor(Treatment,levels=c("Grassland","Biomass Removal","Raking","Raking & Clipping","Hemizonia")),
           y=log(Biomass)))+
  scale_y_continuous(expression(atop(italic("Dittrichia graveolens")~biomass, (log[10](mg)))))+
    xlab("Treatment")+
  ggtitle("")+
  theme_classic(base_size=20)+
  geom_boxplot() #plot data from log(data)
biomass_gg

ggsave(plot=biomass_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/biomass_gg.png",width=25,height=15,units="cm",dpi=800)
  


ggplot(data=reproduction_mydata,
       aes(x=Treatment,
           y=log(Biomass)))+
  geom_boxplot() #plot data from log(data)
  
ggplot(data=reproduction_mydata,
       aes(x=Habitat,
           y=log(Biomass)))+
  geom_boxplot() #plot data from log(data)
```

#Survival Analysis
This code uses NumDaysAlive data with Habitat (roadside and vegetated) and Treatment in a Cox proportional hazards model to assess survival. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. "Surv" creates a survival object to combine the days column (NumDaysAlive) and the reproductive censor column (ReproductionCensor) to be used as a response variable in a model formula. The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: "var1 * var2" will give you the interaction term and the individual variables: so "var1 + var2 + var1:var2". To add random effects, type "+ (1|random effect)". 

##Histograms
```{r}
#Number of Days Alive - All data
hist(mydata$NumDaysAlive,col='steelblue',main='Original') 

#Number of Days Alive - By Habitat
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Habitat)) #Here we see that both Habitats have the same bi-modal distribution.

#Number of Days Alive - By Treatment
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that 4 of the treatments have the same bi-modal distribution and Biomass Removal is right skewed.
```

##Cox Model
Start by making a simple model with no random effects. This will be compared to the full model with random effects.
```{r}
cox_simplemodel1<-coxph(Surv(NumDaysAlive,
                             CensorReproduction)~
                          Habitat*Treatment,
                        data=mydata)
summary(cox_simplemodel1)
print(cox_simplemodel1)
predict(cox_simplemodel1)
hist(predict(cox_simplemodel1))
```

Now make a full model using random effects
```{r}
cox_fullmodel1<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat*Treatment+
                        (1|Site)+
                        (1|Block),
                      data=mydata)
summary(cox_fullmodel1)
print(cox_fullmodel1)
predict(cox_fullmodel1)
hist(predict(cox_fullmodel1))
```

Now we can compare the models to see which model is best
```{r}
anova(cox_simplemodel1,cox_fullmodel1) 
#See example: https://www.rdocumentation.org/packages/coxme/versions/2.2-16/topics/coxme
AIC(cox_simplemodel1,cox_fullmodel1) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here it is the fullmodel1.
```

Now, let's make a model with no interaction term
```{r}
cox_fullmodel2<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel2)
```

Let's compare the the first two models to test for the significance of the term that is removed (using LR)
```{r}
anova(cox_fullmodel1,cox_fullmodel2) #Not significant
AIC(cox_fullmodel1,cox_fullmodel2) #Interaction term is not significant and the second model has a lower AIC score. So we can drop the interaction term and keep fullmodel2.
```

So, now let's add in population nested under site as a random effect
```{r}
cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel3)
```

Now we can compare the models to see which model is best
```{r}
anova(cox_fullmodel2,cox_fullmodel3) #Significant
AIC(cox_fullmodel2,cox_fullmodel3) #Looks like fullmodel3 is the better model because of the lower AIC score
```

###Best Model
```{r}
cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel3)
```

###Risk Assessment
These values are found in the model summary, but if you want to pull them out, here is how you interpret them.
1 = no effect, <1 = decreased risk of death, >1 = increased risk of death.
```{r}
exp(coef(cox_fullmodel3)) #This should be interpreted that Biomass Removal is almost 5% more likely to survive to reproduction compared to Control, and Raking + Clipping is about 2.5% more likely to survive to reproduction compared to Control.
exp(ranef(cox_fullmodel3)$Block)
exp(ranef(cox_fullmodel3)$Site) # Pretty even among all the sites
Anova(cox_fullmodel3)
```

Looks like roadside and offroad plants are the same, so let's combine them together in a model (aka, removing the Habitat term)
```{r}
cox_fullmodel4<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel4)
summary(cox_fullmodel3)
anova(cox_fullmodel3,cox_fullmodel4) #Significant
AIC(cox_fullmodel3,cox_fullmodel4) #Looks like fullmodel4 is the better model because the AIC score is within 2 points of each other, therefore the models are assessed the same and you should take the simpler model. But this is for another manuscript, probably.
```

##ggplot
###Interaction Plot

```{r}
pd<-position_dodge(0)

surv_mydata1<-mydata%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(PropBudSite),
            SD=sd(PropBudSite),
            N=length(PropBudSite))%>%
  mutate(SE=SD/sqrt(N))

field_int_surv_all_gg<-ggplot(data=surv_mydata1,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  ylab("Proportion Survival to Bud")+
  coord_cartesian(ylim=c(0,1))+
  xlab("")+
  theme_classic(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        legend.position = c(0.8, 0.8))+
  scale_y_continuous(name="Proportion Survival to Bud",
                     limits=c(0,1),
                     breaks=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="Proportion Survival to Bud",
       fill="Source Habitat",
       color="Source Habitat",
       shape="Source Habitat")
field_int_surv_all_gg

#ggsave(plot=field_int_surv_all_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_surv_all_gg.png",width=25,height=15,units="cm",dpi=800)

surv_mydata2<-mydata%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(PropBudSite),
            SD=sd(PropBudSite),
            N=length(PropBudSite))%>%
  mutate(SE=SD/sqrt(N))

field_int_surv_all_gg<-ggplot(data=surv_mydata2,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland","Raking","Raking & Clipping","Hemizonia")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  ylab("Proportion Survival to Bud")+
  coord_cartesian(ylim=c(0,1))+
  xlab("")+
  theme_classic(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        legend.position = c(0.8, 0.8))+
  scale_y_continuous(name="Proportion Survival to Bud",
                     limits=c(0,1),
                     breaks=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="Proportion Survival to Bud",
       fill="Source Habitat",
       color="Source Habitat",
       shape="Source Habitat")
field_int_surv_all_gg


survival_gg<-ggplot(data=mydata,
       aes(x=factor(Treatment,levels=c("Grassland","Biomass Removal","Raking","Raking & Clipping","Hemizonia")),
           y=as.numeric(PropBudSite)))+
  labs(y="Proportion Survival to Bud",
       x="Treatment")+
  ggtitle("")+
  theme_classic(base_size=20)+
  geom_boxplot()
survival_gg

ggsave(plot=survival_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_gg.png",width=25,height=15,units="cm",dpi=800)
```

```{r}
boxplot(mydata$PropBudSite~mydata$Treatment,xaxt="n",xlab=NA,ylab="Proportion Survival to Bud",las=1)
axis(1,at=seq(1,5,1),labels=c("Grassland","Biomass\nRemoval","Hemizonia","Raking","Raking &\nClipping"))

boxplot(mydata$PropBudSite~mydata$Treatment,ylab="Proportion Survival to Bud",las=1)
```


##autoplot - Survival Curves
Plot by habitat test
```{r}
pd<-position_dodge(0)

cox_model_data1<-mydata%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")

cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Habitat,
                          data=cox_model_data1)

surv_curve_habitat_treat<-ggplot(data=cox_model_graph1)+
  aes(x=time,
      y=surv,
      shape=strata,
      group=strata,
      color=strata)+
 theme_classic(base_size=16)+
  scale_y_continuous(name="Survival Probability",
                     limits=c(0,1),
                     breaks=seq(0.0,1.0,0.1))+
  scale_x_continuous(name="Survival Time (Days)",
                     limits=c(0,275),
                     breaks=seq(0,275,20))+
  geom_step(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  labs(x="Survival Time (Days)",
       y="Survival Probability")

surv_curve_habitat_treat
```

##autoplot - Survival Curves
Plot by habitat
```{r}
cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Habitat,
                          data=mydata)

autoplot(cox_model_graph1)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme_bw(base_size=16)+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))
```

Plot by all treatment
```{r}
cox_model_graph2<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment,
                          data=mydata)

survival_auto<-autoplot(cox_model_graph2)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
        #scale_fill_discrete(name="Treatment")
survival_auto

ggsave(plot=survival_auto,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_auto.png",width=25,height=15,units="cm",dpi=800)
```

Post hoc Tukey
```{r}
summary(glht(cox_fullmodel4,mcp(Treatment="Tukey")))
```

Plot by 2 treatments (Control and Biomass Removal), and adding in Habitat
```{r}
cox_sub_mydata<-subset(mydata,Treatment%in%c('Control','Biomass Removal'))
cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment,
                          data=cox_sub_mydata)

autoplot(cox_model_graph3)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))

cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment+
                            Habitat,
                          data=cox_sub_mydata)

autoplot(cox_model_graph3)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))
```


Plot by all treatments but not including Hemizonia
```{r}
#Filter the data using subset to remove Hemizonia from the dataframe, but it will remain in the metadata so we'll next use droplevels to reconcile the metadata and the dataframe to only have 4 levels
filtered_data<-subset(mydata,Treatment!="Hemizonia")
filtered_data$Treatment<-droplevels(filtered_data$Treatment)
levels(filtered_data$Treatment)

cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment,
                          data=filtered_data)

survival_filtered<-autoplot(cox_model_graph3)+
  labs(x="Days to Flowering",
       y="Proportion Alive",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
        #scale_fill_discrete(name="Treatment")
survival_filtered

ggsave(plot=survival_filtered,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_filtered.png",width=25,height=15,units="cm",dpi=800)

cox_simple_flower<-coxph(Surv(NumDaysAlive,CensorReproduction)~
                        Treatment,
                      data=filtered_data)
summary(cox_simple_flower)

cox_full_flower<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Treatment+
                        (1|Block),
                      data=filtered_data)
summary(cox_full_flower)

anova(cox_simple_flower,cox_full_flower)
AIC(cox_simple_flower,cox_full_flower) #use the full model, it has the lower AIC score
```

#Using Patchwork to put three graphs together
```{r}
multi_panel_gg<-(field_int_rate_grow_gg+theme(legend.position = c(0.9,1)))/
  (field_int_surv_all_gg+theme(legend.position = "none"))/
  (field_int_bio_grow_gg+theme(legend.position = "none"))+
  plot_annotation(tag_levels = c("A"),
                  tag_suffix = ")")&
  theme(text=element_text(size=10))
multi_panel_gg

ggsave(plot=multi_panel_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/multi_panel_gg.png",width=15,height=25,units="cm",dpi=800)

```




#2022 data

```{r}
mydata_dig<-read.csv("/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/borr_dig_trim_control_2022.csv",stringsAsFactors=T)
mydata_shade<-read.csv("/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/borr_shade_none_2022.csv",stringsAsFactors=T)
mydata_water<-read.csv("/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/borr_water_nutrients_roots_2022.csv",stringsAsFactors=T)
```

Let's manipulate the dig data!

```{r}
pldate_dig<-mydata_dig %>% 
  select(Plant_ID,Row,Treatment,PlantDate.L.,PlantDate.R.)%>% 
  pivot_longer(cols=starts_with("PlantDate"),names_to="Position",values_to="PlantDate",names_prefix="PlantDate.")%>% 
  mutate(Position=str_replace(Position,".$",""))

plmeas_dig<-mydata_dig%>% 
  select(Plant_ID,Row,Treatment,LeftMeasure..mm.,RightMeasure..mm.)%>% 
  rename("Measure_mm_L"="LeftMeasure..mm.","Measure_mm_R"="RightMeasure..mm.") %>%
  pivot_longer(cols=starts_with("Measure"),names_to="Position",values_to="Measurement_mm",names_prefix="Measure_mm_")

plmort_dig<-mydata_dig%>% 
  select(Plant_ID,Row,Treatment,Mortality.Date..L.,Mortality.Date..R.)%>% 
  pivot_longer(cols=starts_with("Mortality"),names_to="Position",values_to="MortalityDate",names_prefix="Mortality.Date..")%>% 
  mutate(Position=str_replace(Position,".$",""))

dig_work<-left_join(pldate_dig,plmeas_dig,by=c("Plant_ID","Row","Treatment","Position"))%>% 
  left_join(.,plmort_dig,by=c("Plant_ID","Row","Treatment","Position"))

library(lubridate)
dig_calc<-dig_work%>% 
  mutate(Censor=1,NumDaysAlive=(as.numeric(yday(mdy(MortalityDate)))-as.numeric(yday(mdy(PlantDate)))))

cox_model_dig<-survfit(Surv(NumDaysAlive,Censor)~
                            Treatment,
                          data=dig_calc)

survival_dig<-autoplot(cox_model_dig)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
        #scale_fill_discrete(name="Treatment")
survival_dig

ggsave(plot=survival_dig,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_dig.png",width=25,height=15,units="cm",dpi=800)
```

Let's manipulate the shade data!

```{r}
pldate_shade<-mydata_shade %>% 
  select(Plant_ID,Plot,Treatment,PlantDate.L.,PlantDate.R.)%>% 
  pivot_longer(cols=starts_with("PlantDate"),names_to="Position",values_to="PlantDate",names_prefix="PlantDate.")%>% 
  mutate(Position=str_replace(Position,".$",""))

plmeas_shade<-mydata_shade%>% 
  select(Plant_ID,Plot,Treatment,LeftMeasure.mm.,RightMeasure.mm.)%>% 
  rename("Measure_mm_L"="LeftMeasure.mm.","Measure_mm_R"="RightMeasure.mm.") %>%
  pivot_longer(cols=starts_with("Measure"),names_to="Position",values_to="Measurement_mm",names_prefix="Measure_mm_")

plmort_shade<-mydata_shade%>% 
  select(Plant_ID,Plot,Treatment,Mortality.Date..L.,Mortality.Date..R.)%>% 
  pivot_longer(cols=starts_with("Mortality"),names_to="Position",values_to="MortalityDate",names_prefix="Mortality.Date..")%>% 
  mutate(Position=str_replace(Position,".$",""))

shade_work<-left_join(pldate_shade,plmeas_shade,by=c("Plant_ID","Plot","Treatment","Position"))%>% 
  left_join(.,plmort_shade,by=c("Plant_ID","Plot","Treatment","Position"))

library(lubridate)
shade_calc<-shade_work%>% 
  mutate(Censor=1,NumDaysAlive=(as.numeric(yday(mdy(MortalityDate)))-as.numeric(yday(mdy(PlantDate)))))

cox_model_shade<-survfit(Surv(NumDaysAlive,Censor)~
                            Treatment,
                          data=shade_calc)

survival_shade<-autoplot(cox_model_shade)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
survival_shade

ggsave(plot=survival_shade,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_shade.png",width=25,height=15,units="cm",dpi=800)
```


Let's manipulate the water data!

```{r}
pldate_water<-mydata_water %>% 
  select(Plant_ID,Plot,Competition,Treatment,PlantDate.L.,PlantDate.R.)%>% 
  pivot_longer(cols=starts_with("PlantDate"),names_to="Position",values_to="PlantDate",names_prefix="PlantDate.")%>% 
  mutate(Position=str_replace(Position,".$",""))

plmeas_water<-mydata_water%>% 
  select(Plant_ID,Plot,Competition,Treatment,LeftMeasure.mm.,RightMeasure.mm.)%>% 
  rename("Measure_mm_L"="LeftMeasure.mm.","Measure_mm_R"="RightMeasure.mm.") %>%
  pivot_longer(cols=starts_with("Measure"),names_to="Position",values_to="Measurement_mm",names_prefix="Measure_mm_")

plmort_water<-mydata_water%>% 
  select(Plant_ID,Plot,Competition,Treatment,Mortality.Date..L.,Mortality.Date..R.)%>% 
  pivot_longer(cols=starts_with("Mortality"),names_to="Position",values_to="MortalityDate",names_prefix="Mortality.Date..")%>% 
  mutate(Position=str_replace(Position,".$",""))

water_work<-left_join(pldate_water,plmeas_water,by=c("Plant_ID","Plot","Competition","Treatment","Position"))%>% 
  left_join(.,plmort_water,by=c("Plant_ID","Plot","Competition","Treatment","Position"))

library(lubridate)
water_calc<-water_work%>% 
  mutate(Censor=1,NumDaysAlive=(as.numeric(yday(mdy(MortalityDate)))-as.numeric(yday(mdy(PlantDate)))))

cox_model_water<-survfit(Surv(NumDaysAlive,Censor)~
                            Treatment+Competition,
                          data=water_calc)

survival_water<-autoplot(cox_model_water)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
        #scale_fill_discrete(name="Treatment")
survival_water

ggsave(plot=survival_water,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_water.png",width=25,height=15,units="cm",dpi=800)
```


```{r}
#Now just looking at each treatment with weed cloth or no weed cloth present

water_calc_w<-water_calc%>% 
  filter(Treatment=="Water")
cox_model_Twater<-survfit(Surv(NumDaysAlive,Censor)~
                            Competition,
                          data=water_calc_w)

survival_Twater<-autoplot(cox_model_Twater)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
        #scale_fill_discrete(name="Treatment")
survival_Twater

ggsave(plot=survival_Twater,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Twater.png",width=25,height=15,units="cm",dpi=800)

water_calc_f<-water_calc%>% 
  filter(Treatment=="Fertilizer")
cox_model_Tfertilizer<-survfit(Surv(NumDaysAlive,Censor)~
                            Competition,
                          data=water_calc_f)

survival_Tfertilizer<-autoplot(cox_model_Tfertilizer)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
        #scale_fill_discrete(name="Treatment")
survival_Tfertilizer

ggsave(plot=survival_Tfertilizer,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Tfertilizer.png",width=25,height=15,units="cm",dpi=800)

water_calc_c<-water_calc%>% 
  filter(Treatment=="Control")
cox_model_Tcontrol<-survfit(Surv(NumDaysAlive,Censor)~
                            Competition,
                          data=water_calc_c)

survival_Tcontrol<-autoplot(cox_model_Tcontrol)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
        #scale_fill_discrete(name="Treatment")
survival_Tcontrol

ggsave(plot=survival_Tcontrol,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Tcontrol.png",width=25,height=15,units="cm",dpi=800)
```

```{r}
#Now just looking at each weed cloth or no weed cloth present for all treatments

water_calc_wt<-water_calc%>% 
  filter(Competition=="Weed Cloth")
cox_model_Weedt<-survfit(Surv(NumDaysAlive,Censor)~
                            Treatment,
                          data=water_calc_wt)

survival_Weedt<-autoplot(cox_model_Weedt)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
survival_Weedt

ggsave(plot=survival_Weedt,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Weedt.png",width=25,height=15,units="cm",dpi=800)

water_calc_Nt<-water_calc%>% 
  filter(Competition=="No Cloth")
cox_model_Nonet<-survfit(Surv(NumDaysAlive,Censor)~
                            Treatment,
                          data=water_calc_Nt)

survival_Nonet<-autoplot(cox_model_Nonet)+
  labs(x="Time (Days)",
       y="Survival",
       title="")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=20),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=20),
        legend.title=element_text(face="bold",
                                  size=12))
survival_Nonet

ggsave(plot=survival_Nonet,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/survival_Nonet.png",width=25,height=15,units="cm",dpi=800)

library(patchwork)
library(ggplot2)
combined_plot<-survival_Weedt+survival_Nonet
combined_plot

#Try this from Julia
#WH_PA + WH_subset + choice + plot_layout(guides = 'collect') +
#  plot_annotation(tag_levels = 'A', tag_suffix = ')') & 
#   theme(plot.tag.position = c(0, 1),
#         plot.tag = element_text(size = 11.5, hjust = 0, vjust = 0))
```

##Histograms
```{r}
#Number of Days Alive - By Treatment
ggplot(shade_calc,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that the 3 treatments are right skewed.

ggplot(dig_calc,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that the 3 treatments are right skewed.

ggplot(water_calc,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that the 3 treatments are right skewed.
```

##Cox Model
Start by making a simple model with no random effects. This will be compared to the full model with random effects.
```{r}
cox_simp_shade<-coxph(Surv(NumDaysAlive,
                             Censor)~
                          Treatment,
                        data=shade_calc)
summary(cox_simp_shade)
print(cox_simp_shade)
predict(cox_simp_shade)
hist(predict(cox_simp_shade))

cox_simp_dig<-coxph(Surv(NumDaysAlive,
                             Censor)~
                          Treatment,
                        data=dig_calc)
summary(cox_simp_dig)
print(cox_simp_dig)
predict(cox_simp_dig)
hist(predict(cox_simp_dig))

cox_simp_water<-coxph(Surv(NumDaysAlive,
                             Censor)~
                          Treatment*Competition,
                        data=water_calc)
summary(cox_simp_water)
print(cox_simp_water)
predict(cox_simp_water)
hist(predict(cox_simp_water))
```

Now make a full model using random effects
```{r}
cox_full_dig<-coxme(Surv(NumDaysAlive,Censor)~
                        Treatment+
                        (1|Row),
                      data=dig_calc)
summary(cox_full_dig)
print(cox_full_dig)
predict(cox_full_dig)
hist(predict(cox_full_dig))

cox_full_shade<-coxme(Surv(NumDaysAlive,Censor)~
                        Treatment+
                        (1|Plot),
                      data=shade_calc)
summary(cox_full_shade)
print(cox_full_shade)
predict(cox_full_shade)
hist(predict(cox_full_shade))

cox_full_water<-coxme(Surv(NumDaysAlive,Censor)~
                        Treatment*Competition+
                        (1|Plot),
                      data=water_calc)
summary(cox_full_water)
print(cox_full_water)
predict(cox_full_water)
hist(predict(cox_full_water))

cox_full_water1<-coxme(Surv(NumDaysAlive,Censor)~
                        Treatment+
                        Competition+
                        (1|Plot),
                      data=water_calc)
summary(cox_full_water1)
print(cox_full_water1)
predict(cox_full_water1)
hist(predict(cox_full_water1))

coef_values <- c(-0.2301898, -0.1632106, -0.4224990)
se_values <- c(0.1369386, 0.1401706, 0.1157955)

# Calculate z and p values
z_values <- coef_values / se_values
p_values <- 2 * (1 - pnorm(abs(z_values)))

# Print the calculated z and p values
z_values
p_values

1-exp(coef(cox_full_water1))


cox_full_water_notreatment<-coxme(Surv(NumDaysAlive,Censor)~
                                    Competition+
                                    (1|Plot),
                                  data=water_calc)

cox_full_water_nocompetition<-coxme(Surv(NumDaysAlive,Censor)~
                                      Treatment+
                                      (1|Plot),
                                    data=water_calc)

anova(cox_full_water1,cox_full_water_notreatment) # effect of treatment (using cox_full_water1 here assuming you have no interaction effect)

anova(cox_full_water1,cox_full_water_nocompetition) # effect of competition

#Now if you want to do a post-hoc test of your different Treatment levels, you can use this:

library(multcomp)

summary(glht(cox_full_water1, mcp(Treatment="Tukey"))) # for treatment

summary(glht(cox_full_water1, mcp(Competition="Tukey"))) # for competition – you have only 2 levels so this should give you the same result as the main effect.
```

Now we can compare the models to see which model is best
```{r}
anova(cox_simp_dig,cox_full_dig) 
AIC(cox_simp_dig,cox_full_dig) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here the AIC scores are the same, so parsimony says to keep the simple model.

anova(cox_simp_shade,cox_full_shade) 
AIC(cox_simp_shade,cox_full_shade) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here it is the full model because the AIC score is smaller.

anova(cox_simp_water,cox_full_water1,cox_full_water) 
AIC(cox_simp_water,cox_full_water1,cox_full_water) #But comparing the AIC scores is easiest. Use the middle model (cox_full_water1) with the lowest AIC.

anova(cox_full_water,cox_full_water1)
summary(glht(cox_full_water, mcp(Competition:Treatment="Tukey")))
```
